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    "<a id='top'></a>\n",
    "<div class=\"list-group\" id=\"list-tab\" role=\"tablist\">\n",
    "    \n",
    "<h1 style=\"padding: 8px;color:white; display:fill;background-color:#555555; border-radius:5px; font-size:150%\"><b> Table of contents </b></h1>\n",
    "\n",
    " - [**Introduction**](#1)\n",
    "\n",
    " - [**Import and Set Up**](#2)\n",
    " \n",
    " - [**Pre-processing and feature selection**](#3)\n",
    "    \n",
    " - [**Modelling and Evaluation**](#4)\n",
    "    - [**Logistical Classification**](#4_1)\n",
    "    - [**kNN**](#4_2)\n",
    "    - [**Decision Tree**](#4_3)\n",
    "    - [**Extra Trees**](#4_4)\n",
    "    - [**Random Forest**](#4_5)\n",
    "    - [**Gradient Boosting Classifier**](#4_6)\n",
    "    - [**Neural Network MLP**](#4_7)\n",
    "    - [**Neural Network MLP (Keras)**](#4_8)\n",
    "    - [**GRU (Keras)**](#4_9)\n",
    "    - [**LSTM (Keras)**](#4_10)\n",
    "    \n",
    " - [**Evaluate**](#5)\n",
    "   "
   ]
  },
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    "<a id='1'></a>\n",
    "# <p style=\"padding: 8px;color:white; display:fill;background-color:#555555; border-radius:5px; font-size:100%\"> <b>Introduction</b>"
   ]
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   "source": [
    "This Notebook is about transforming and modelling the data. A previous notebook of mine did the EDA using pandas-profiling and sweetviz. So its not great to load here.\n",
    "\n",
    "The study has an all-in approach (using all descriptive features). It can be improved by adding back propagation and removing the features with high P-values. I did not have the time or motivation to do this as well. "
   ]
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   "source": [
    "<a id='2'></a>\n",
    "# <p style=\"padding: 8px;color:white; display:fill;background-color:#555555; border-radius:5px; font-size:100%\"> <b>Import and Set up</b>"
   ]
  },
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# from IPython.core.display import display, HTML\n",
    "import plotly.graph_objects as go\n",
    "# import plotly.express as px\n",
    "# from plotly.subplots import make_subplots\n",
    "# import plotly.io as pio\n",
    "\n",
    "\n",
    "# import seaborn as sns\n",
    "# # from importlib import reload\n",
    "# import matplotlib.pyplot as plt\n",
    "# # import warnings\n",
    "\n",
    "# # Configure Jupyter Notebook\n",
    "# pd.set_option('display.max_columns', None) \n",
    "# pd.set_option('display.max_rows', 500) \n",
    "# pd.set_option('display.expand_frame_repr', False)\n",
    "# # pd.set_option('max_colwidth', -1)\n",
    "# display(HTML(\"<style>div.output_scroll { height: 35em; }</style>\"))\n",
    "\n",
    "# reload(plt)\n",
    "# %matplotlib inline\n",
    "# %config InlineBackend.figure_format ='retina'\n",
    "\n",
    "# warnings.filterwarnings('ignore')\n",
    "\n",
    "# # configure plotly graph objects\n",
    "# pio.renderers.default = 'iframe'\n",
    "# # pio.renderers.default = 'vscode'\n",
    "\n",
    "# pio.templates[\"ck_template\"] = go.layout.Template(\n",
    "#     layout_colorway = px.colors.sequential.Viridis, \n",
    "# #     layout_hovermode = 'closest',\n",
    "# #     layout_hoverdistance = -1,\n",
    "#     layout_autosize=False,\n",
    "#     layout_width=800,\n",
    "#     layout_height=600,\n",
    "#     layout_font = dict(family=\"Calibri Light\"),\n",
    "#     layout_title_font = dict(family=\"Calibri\"),\n",
    "#     layout_hoverlabel_font = dict(family=\"Calibri Light\"),\n",
    "# #     plot_bgcolor=\"white\",\n",
    "# )\n",
    " \n",
    "# # pio.templates.default = 'seaborn+ck_template+gridon'\n",
    "# pio.templates.default = 'ck_template+gridon'\n",
    "# # pio.templates.default = 'seaborn+gridon'\n",
    "# # pio.templates"
   ]
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   "source": [
    "df = pd.read_csv('UNSW_NB15_training-set0.csv')"
   ]
  },
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 82332 entries, 0 to 82331\n",
      "Data columns (total 45 columns):\n",
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      "---  ------             --------------  -----  \n",
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      " 2   proto              82332 non-null  object \n",
      " 3   service            82332 non-null  object \n",
      " 4   state              82332 non-null  object \n",
      " 5   spkts              82332 non-null  int64  \n",
      " 6   dpkts              82332 non-null  int64  \n",
      " 7   sbytes             82332 non-null  int64  \n",
      " 8   dbytes             82332 non-null  int64  \n",
      " 9   rate               82332 non-null  float64\n",
      " 10  sttl               82332 non-null  int64  \n",
      " 11  dttl               82332 non-null  int64  \n",
      " 12  sload              82332 non-null  float64\n",
      " 13  dload              82332 non-null  float64\n",
      " 14  sloss              82332 non-null  int64  \n",
      " 15  dloss              82332 non-null  int64  \n",
      " 16  sinpkt             82332 non-null  float64\n",
      " 17  dinpkt             82332 non-null  float64\n",
      " 18  sjit               82332 non-null  float64\n",
      " 19  djit               82332 non-null  float64\n",
      " 20  swin               82332 non-null  int64  \n",
      " 21  stcpb              82332 non-null  int64  \n",
      " 22  dtcpb              82332 non-null  int64  \n",
      " 23  dwin               82332 non-null  int64  \n",
      " 24  tcprtt             82332 non-null  float64\n",
      " 25  synack             82332 non-null  float64\n",
      " 26  ackdat             82332 non-null  float64\n",
      " 27  smean              82332 non-null  int64  \n",
      " 28  dmean              82332 non-null  int64  \n",
      " 29  trans_depth        82332 non-null  int64  \n",
      " 30  response_body_len  82332 non-null  int64  \n",
      " 31  ct_srv_src         82332 non-null  int64  \n",
      " 32  ct_state_ttl       82332 non-null  int64  \n",
      " 33  ct_dst_ltm         82332 non-null  int64  \n",
      " 34  ct_src_dport_ltm   82332 non-null  int64  \n",
      " 35  ct_dst_sport_ltm   82332 non-null  int64  \n",
      " 36  ct_dst_src_ltm     82332 non-null  int64  \n",
      " 37  is_ftp_login       82332 non-null  int64  \n",
      " 38  ct_ftp_cmd         82332 non-null  int64  \n",
      " 39  ct_flw_http_mthd   82332 non-null  int64  \n",
      " 40  ct_src_ltm         82332 non-null  int64  \n",
      " 41  ct_srv_dst         82332 non-null  int64  \n",
      " 42  is_sm_ips_ports    82332 non-null  int64  \n",
      " 43  attack_cat         82332 non-null  object \n",
      " 44  label              82332 non-null  int64  \n",
      "dtypes: float64(11), int64(30), object(4)\n",
      "memory usage: 28.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>udp</td>\n",
       "      <td>-</td>\n",
       "      <td>INT</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2126</td>\n",
       "      <td>0</td>\n",
       "      <td>100000.00250</td>\n",
       "      <td>254</td>\n",
       "      <td>0</td>\n",
       "      <td>8.504000e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1063</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>udp</td>\n",
       "      <td>-</td>\n",
       "      <td>INT</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>784</td>\n",
       "      <td>0</td>\n",
       "      <td>333333.32150</td>\n",
       "      <td>254</td>\n",
       "      <td>0</td>\n",
       "      <td>1.045333e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>392</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>udp</td>\n",
       "      <td>-</td>\n",
       "      <td>INT</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1960</td>\n",
       "      <td>0</td>\n",
       "      <td>166666.66080</td>\n",
       "      <td>254</td>\n",
       "      <td>0</td>\n",
       "      <td>1.306667e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>980</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>udp</td>\n",
       "      <td>-</td>\n",
       "      <td>INT</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1384</td>\n",
       "      <td>0</td>\n",
       "      <td>35714.28522</td>\n",
       "      <td>254</td>\n",
       "      <td>0</td>\n",
       "      <td>1.977143e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.028</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>692</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>arp</td>\n",
       "      <td>-</td>\n",
       "      <td>INT</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60000.688</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>arp</td>\n",
       "      <td>-</td>\n",
       "      <td>INT</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60000.712</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Normal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id       dur proto service state  spkts  dpkts  sbytes  dbytes          rate  sttl  dttl         sload  dload  sloss  dloss     sinpkt  dinpkt  sjit  djit  swin  stcpb  dtcpb  dwin  tcprtt  synack  ackdat  smean  dmean  trans_depth  response_body_len  ct_srv_src  ct_state_ttl  ct_dst_ltm  ct_src_dport_ltm  ct_dst_sport_ltm  ct_dst_src_ltm  is_ftp_login  ct_ftp_cmd  ct_flw_http_mthd  ct_src_ltm  ct_srv_dst  is_sm_ips_ports attack_cat  label\n",
       "0   1  0.000011   udp       -   INT      2      0     496       0   90909.09020   254     0  1.803636e+08    0.0      0      0      0.011     0.0   0.0   0.0     0      0      0     0     0.0     0.0     0.0    248      0            0                  0           2             2           1                 1                 1               2             0           0                 0           1           2                0     Normal      0\n",
       "1   2  0.000008   udp       -   INT      2      0    1762       0  125000.00030   254     0  8.810000e+08    0.0      0      0      0.008     0.0   0.0   0.0     0      0      0     0     0.0     0.0     0.0    881      0            0                  0           2             2           1                 1                 1               2             0           0                 0           1           2                0     Normal      0\n",
       "2   3  0.000005   udp       -   INT      2      0    1068       0  200000.00510   254     0  8.544000e+08    0.0      0      0      0.005     0.0   0.0   0.0     0      0      0     0     0.0     0.0     0.0    534      0            0                  0           3             2           1                 1                 1               3             0           0                 0           1           3                0     Normal      0\n",
       "3   4  0.000006   udp       -   INT      2      0     900       0  166666.66080   254     0  6.000000e+08    0.0      0      0      0.006     0.0   0.0   0.0     0      0      0     0     0.0     0.0     0.0    450      0            0                  0           3             2           2                 2                 1               3             0           0                 0           2           3                0     Normal      0\n",
       "4   5  0.000010   udp       -   INT      2      0    2126       0  100000.00250   254     0  8.504000e+08    0.0      0      0      0.010     0.0   0.0   0.0     0      0      0     0     0.0     0.0     0.0   1063      0            0                  0           3             2           2                 2                 1               3             0           0                 0           2           3                0     Normal      0\n",
       "5   6  0.000003   udp       -   INT      2      0     784       0  333333.32150   254     0  1.045333e+09    0.0      0      0      0.003     0.0   0.0   0.0     0      0      0     0     0.0     0.0     0.0    392      0            0                  0           2             2           2                 2                 1               2             0           0                 0           2           2                0     Normal      0\n",
       "6   7  0.000006   udp       -   INT      2      0    1960       0  166666.66080   254     0  1.306667e+09    0.0      0      0      0.006     0.0   0.0   0.0     0      0      0     0     0.0     0.0     0.0    980      0            0                  0           2             2           2                 2                 1               2             0           0                 0           2           2                0     Normal      0\n",
       "7   8  0.000028   udp       -   INT      2      0    1384       0   35714.28522   254     0  1.977143e+08    0.0      0      0      0.028     0.0   0.0   0.0     0      0      0     0     0.0     0.0     0.0    692      0            0                  0           3             2           1                 1                 1               3             0           0                 0           1           3                0     Normal      0\n",
       "8   9  0.000000   arp       -   INT      1      0      46       0       0.00000     0     0  0.000000e+00    0.0      0      0  60000.688     0.0   0.0   0.0     0      0      0     0     0.0     0.0     0.0     46      0            0                  0           2             2           2                 2                 2               2             0           0                 0           2           2                1     Normal      0\n",
       "9  10  0.000000   arp       -   INT      1      0      46       0       0.00000     0     0  0.000000e+00    0.0      0      0  60000.712     0.0   0.0   0.0     0      0      0     0     0.0     0.0     0.0     46      0            0                  0           2             2           2                 2                 2               2             0           0                 0           2           2                1     Normal      0"
      ]
     },
     "execution_count": 456,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "id": "737a3ad6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:02.517909Z",
     "iopub.status.busy": "2022-11-10T04:23:02.517126Z",
     "iopub.status.idle": "2022-11-10T04:23:02.841869Z",
     "shell.execute_reply": "2022-11-10T04:23:02.841252Z"
    },
    "papermill": {
     "duration": 0.423355,
     "end_time": "2022-11-10T04:23:02.845622",
     "exception": false,
     "start_time": "2022-11-10T04:23:02.422267",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>dur</th>\n",
       "      <th>proto</th>\n",
       "      <th>service</th>\n",
       "      <th>state</th>\n",
       "      <th>spkts</th>\n",
       "      <th>dpkts</th>\n",
       "      <th>sbytes</th>\n",
       "      <th>dbytes</th>\n",
       "      <th>rate</th>\n",
       "      <th>sttl</th>\n",
       "      <th>dttl</th>\n",
       "      <th>sload</th>\n",
       "      <th>dload</th>\n",
       "      <th>sloss</th>\n",
       "      <th>dloss</th>\n",
       "      <th>sinpkt</th>\n",
       "      <th>dinpkt</th>\n",
       "      <th>sjit</th>\n",
       "      <th>djit</th>\n",
       "      <th>swin</th>\n",
       "      <th>stcpb</th>\n",
       "      <th>dtcpb</th>\n",
       "      <th>dwin</th>\n",
       "      <th>tcprtt</th>\n",
       "      <th>synack</th>\n",
       "      <th>ackdat</th>\n",
       "      <th>smean</th>\n",
       "      <th>dmean</th>\n",
       "      <th>trans_depth</th>\n",
       "      <th>response_body_len</th>\n",
       "      <th>ct_srv_src</th>\n",
       "      <th>ct_state_ttl</th>\n",
       "      <th>ct_dst_ltm</th>\n",
       "      <th>ct_src_dport_ltm</th>\n",
       "      <th>ct_dst_sport_ltm</th>\n",
       "      <th>ct_dst_src_ltm</th>\n",
       "      <th>is_ftp_login</th>\n",
       "      <th>ct_ftp_cmd</th>\n",
       "      <th>ct_flw_http_mthd</th>\n",
       "      <th>ct_src_ltm</th>\n",
       "      <th>ct_srv_dst</th>\n",
       "      <th>is_sm_ips_ports</th>\n",
       "      <th>attack_cat</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332</td>\n",
       "      <td>82332</td>\n",
       "      <td>82332</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.00000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>82332.00000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332</td>\n",
       "      <td>82332.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tcp</td>\n",
       "      <td>-</td>\n",
       "      <td>FIN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Normal</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43095</td>\n",
       "      <td>47153</td>\n",
       "      <td>39339</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41166.500000</td>\n",
       "      <td>1.006756</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.666472</td>\n",
       "      <td>17.545936</td>\n",
       "      <td>7.993908e+03</td>\n",
       "      <td>1.323379e+04</td>\n",
       "      <td>8.241089e+04</td>\n",
       "      <td>180.967667</td>\n",
       "      <td>95.713003</td>\n",
       "      <td>6.454902e+07</td>\n",
       "      <td>6.305470e+05</td>\n",
       "      <td>4.753692</td>\n",
       "      <td>6.308556</td>\n",
       "      <td>755.394301</td>\n",
       "      <td>121.701284</td>\n",
       "      <td>6.363075e+03</td>\n",
       "      <td>535.180430</td>\n",
       "      <td>133.45908</td>\n",
       "      <td>1.084642e+09</td>\n",
       "      <td>1.073465e+09</td>\n",
       "      <td>128.28662</td>\n",
       "      <td>0.055925</td>\n",
       "      <td>0.029256</td>\n",
       "      <td>0.026669</td>\n",
       "      <td>139.528604</td>\n",
       "      <td>116.275069</td>\n",
       "      <td>0.094277</td>\n",
       "      <td>1.595372e+03</td>\n",
       "      <td>9.546604</td>\n",
       "      <td>1.369273</td>\n",
       "      <td>5.744923</td>\n",
       "      <td>4.928898</td>\n",
       "      <td>3.663011</td>\n",
       "      <td>7.456360</td>\n",
       "      <td>0.008284</td>\n",
       "      <td>0.008381</td>\n",
       "      <td>0.129743</td>\n",
       "      <td>6.468360</td>\n",
       "      <td>9.164262</td>\n",
       "      <td>0.011126</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.550600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>23767.345519</td>\n",
       "      <td>4.710444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133.916353</td>\n",
       "      <td>115.574086</td>\n",
       "      <td>1.716423e+05</td>\n",
       "      <td>1.514715e+05</td>\n",
       "      <td>1.486204e+05</td>\n",
       "      <td>101.513358</td>\n",
       "      <td>116.667722</td>\n",
       "      <td>1.798618e+08</td>\n",
       "      <td>2.393001e+06</td>\n",
       "      <td>64.649620</td>\n",
       "      <td>55.708021</td>\n",
       "      <td>6182.615732</td>\n",
       "      <td>1292.378499</td>\n",
       "      <td>5.672402e+04</td>\n",
       "      <td>3635.305383</td>\n",
       "      <td>127.35700</td>\n",
       "      <td>1.390860e+09</td>\n",
       "      <td>1.381996e+09</td>\n",
       "      <td>127.49137</td>\n",
       "      <td>0.116022</td>\n",
       "      <td>0.070854</td>\n",
       "      <td>0.055094</td>\n",
       "      <td>208.472063</td>\n",
       "      <td>244.600271</td>\n",
       "      <td>0.542922</td>\n",
       "      <td>3.806697e+04</td>\n",
       "      <td>11.090289</td>\n",
       "      <td>1.067188</td>\n",
       "      <td>8.418112</td>\n",
       "      <td>8.389545</td>\n",
       "      <td>5.915386</td>\n",
       "      <td>11.415191</td>\n",
       "      <td>0.091171</td>\n",
       "      <td>0.092485</td>\n",
       "      <td>0.638683</td>\n",
       "      <td>8.543927</td>\n",
       "      <td>11.121413</td>\n",
       "      <td>0.104891</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.497436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20583.750000</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.140000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.860611e+01</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.120247e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41166.500000</td>\n",
       "      <td>0.014138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.340000e+02</td>\n",
       "      <td>1.780000e+02</td>\n",
       "      <td>2.650177e+03</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.770032e+05</td>\n",
       "      <td>2.112951e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.557929</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>1.762392e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>2.788886e+07</td>\n",
       "      <td>2.856975e+07</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>0.000551</td>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61749.250000</td>\n",
       "      <td>0.719360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.280000e+03</td>\n",
       "      <td>9.560000e+02</td>\n",
       "      <td>1.111111e+05</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>6.514286e+07</td>\n",
       "      <td>1.585808e+04</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>63.409444</td>\n",
       "      <td>63.136369</td>\n",
       "      <td>3.219332e+03</td>\n",
       "      <td>128.459914</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>2.171310e+09</td>\n",
       "      <td>2.144205e+09</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>0.105541</td>\n",
       "      <td>0.052596</td>\n",
       "      <td>0.048816</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82332.000000</td>\n",
       "      <td>59.999989</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10646.000000</td>\n",
       "      <td>11018.000000</td>\n",
       "      <td>1.435577e+07</td>\n",
       "      <td>1.465753e+07</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>5.268000e+09</td>\n",
       "      <td>2.082111e+07</td>\n",
       "      <td>5319.000000</td>\n",
       "      <td>5507.000000</td>\n",
       "      <td>60009.992000</td>\n",
       "      <td>57739.240000</td>\n",
       "      <td>1.483831e+06</td>\n",
       "      <td>463199.240100</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>4.294950e+09</td>\n",
       "      <td>4.294881e+09</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>3.821465</td>\n",
       "      <td>3.226788</td>\n",
       "      <td>2.928778</td>\n",
       "      <td>1504.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>5.242880e+06</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id           dur  proto service  state         spkts         dpkts        sbytes        dbytes          rate          sttl          dttl         sload         dload         sloss         dloss        sinpkt        dinpkt          sjit           djit         swin         stcpb         dtcpb         dwin        tcprtt        synack        ackdat         smean         dmean   trans_depth  response_body_len    ct_srv_src  ct_state_ttl    ct_dst_ltm  ct_src_dport_ltm  ct_dst_sport_ltm  ct_dst_src_ltm  is_ftp_login    ct_ftp_cmd  ct_flw_http_mthd    ct_src_ltm    ct_srv_dst  is_sm_ips_ports attack_cat         label\n",
       "count   82332.000000  82332.000000  82332   82332  82332  82332.000000  82332.000000  8.233200e+04  8.233200e+04  8.233200e+04  82332.000000  82332.000000  8.233200e+04  8.233200e+04  82332.000000  82332.000000  82332.000000  82332.000000  8.233200e+04   82332.000000  82332.00000  8.233200e+04  8.233200e+04  82332.00000  82332.000000  82332.000000  82332.000000  82332.000000  82332.000000  82332.000000       8.233200e+04  82332.000000  82332.000000  82332.000000      82332.000000      82332.000000    82332.000000  82332.000000  82332.000000      82332.000000  82332.000000  82332.000000     82332.000000      82332  82332.000000\n",
       "unique           NaN           NaN    131      13      7           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN            NaN          NaN           NaN           NaN          NaN           NaN           NaN           NaN           NaN           NaN           NaN                NaN           NaN           NaN           NaN               NaN               NaN             NaN           NaN           NaN               NaN           NaN           NaN              NaN         10           NaN\n",
       "top              NaN           NaN    tcp       -    FIN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN            NaN          NaN           NaN           NaN          NaN           NaN           NaN           NaN           NaN           NaN           NaN                NaN           NaN           NaN           NaN               NaN               NaN             NaN           NaN           NaN               NaN           NaN           NaN              NaN     Normal           NaN\n",
       "freq             NaN           NaN  43095   47153  39339           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN           NaN            NaN          NaN           NaN           NaN          NaN           NaN           NaN           NaN           NaN           NaN           NaN                NaN           NaN           NaN           NaN               NaN               NaN             NaN           NaN           NaN               NaN           NaN           NaN              NaN      37000           NaN\n",
       "mean    41166.500000      1.006756    NaN     NaN    NaN     18.666472     17.545936  7.993908e+03  1.323379e+04  8.241089e+04    180.967667     95.713003  6.454902e+07  6.305470e+05      4.753692      6.308556    755.394301    121.701284  6.363075e+03     535.180430    133.45908  1.084642e+09  1.073465e+09    128.28662      0.055925      0.029256      0.026669    139.528604    116.275069      0.094277       1.595372e+03      9.546604      1.369273      5.744923          4.928898          3.663011        7.456360      0.008284      0.008381          0.129743      6.468360      9.164262         0.011126        NaN      0.550600\n",
       "std     23767.345519      4.710444    NaN     NaN    NaN    133.916353    115.574086  1.716423e+05  1.514715e+05  1.486204e+05    101.513358    116.667722  1.798618e+08  2.393001e+06     64.649620     55.708021   6182.615732   1292.378499  5.672402e+04    3635.305383    127.35700  1.390860e+09  1.381996e+09    127.49137      0.116022      0.070854      0.055094    208.472063    244.600271      0.542922       3.806697e+04     11.090289      1.067188      8.418112          8.389545          5.915386       11.415191      0.091171      0.092485          0.638683      8.543927     11.121413         0.104891        NaN      0.497436\n",
       "min         1.000000      0.000000    NaN     NaN    NaN      1.000000      0.000000  2.400000e+01  0.000000e+00  0.000000e+00      0.000000      0.000000  0.000000e+00  0.000000e+00      0.000000      0.000000      0.000000      0.000000  0.000000e+00       0.000000      0.00000  0.000000e+00  0.000000e+00      0.00000      0.000000      0.000000      0.000000     24.000000      0.000000      0.000000       0.000000e+00      1.000000      0.000000      1.000000          1.000000          1.000000        1.000000      0.000000      0.000000          0.000000      1.000000      1.000000         0.000000        NaN      0.000000\n",
       "25%     20583.750000      0.000008    NaN     NaN    NaN      2.000000      0.000000  1.140000e+02  0.000000e+00  2.860611e+01     62.000000      0.000000  1.120247e+04  0.000000e+00      0.000000      0.000000      0.008000      0.000000  0.000000e+00       0.000000      0.00000  0.000000e+00  0.000000e+00      0.00000      0.000000      0.000000      0.000000     57.000000      0.000000      0.000000       0.000000e+00      2.000000      1.000000      1.000000          1.000000          1.000000        1.000000      0.000000      0.000000          0.000000      1.000000      2.000000         0.000000        NaN      0.000000\n",
       "50%     41166.500000      0.014138    NaN     NaN    NaN      6.000000      2.000000  5.340000e+02  1.780000e+02  2.650177e+03    254.000000     29.000000  5.770032e+05  2.112951e+03      1.000000      0.000000      0.557929      0.010000  1.762392e+01       0.000000    255.00000  2.788886e+07  2.856975e+07    255.00000      0.000551      0.000441      0.000080     65.000000     44.000000      0.000000       0.000000e+00      5.000000      1.000000      2.000000          1.000000          1.000000        3.000000      0.000000      0.000000          0.000000      3.000000      5.000000         0.000000        NaN      1.000000\n",
       "75%     61749.250000      0.719360    NaN     NaN    NaN     12.000000     10.000000  1.280000e+03  9.560000e+02  1.111111e+05    254.000000    252.000000  6.514286e+07  1.585808e+04      3.000000      2.000000     63.409444     63.136369  3.219332e+03     128.459914    255.00000  2.171310e+09  2.144205e+09    255.00000      0.105541      0.052596      0.048816    100.000000     87.000000      0.000000       0.000000e+00     11.000000      2.000000      6.000000          4.000000          3.000000        6.000000      0.000000      0.000000          0.000000      7.000000     11.000000         0.000000        NaN      1.000000\n",
       "max     82332.000000     59.999989    NaN     NaN    NaN  10646.000000  11018.000000  1.435577e+07  1.465753e+07  1.000000e+06    255.000000    253.000000  5.268000e+09  2.082111e+07   5319.000000   5507.000000  60009.992000  57739.240000  1.483831e+06  463199.240100    255.00000  4.294950e+09  4.294881e+09    255.00000      3.821465      3.226788      2.928778   1504.000000   1500.000000    131.000000       5.242880e+06     63.000000      6.000000     59.000000         59.000000         38.000000       63.000000      2.000000      2.000000         16.000000     60.000000     62.000000         1.000000        NaN      1.000000"
      ]
     },
     "execution_count": 457,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "128cddbf",
   "metadata": {
    "papermill": {
     "duration": 0.081464,
     "end_time": "2022-11-10T04:23:03.011898",
     "exception": false,
     "start_time": "2022-11-10T04:23:02.930434",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id='3'></a>\n",
    "# <p style=\"padding: 8px;color:white; display:fill;background-color:#555555; border-radius:5px; font-size:100%\"> <b>Pre-processing and Feature Selection</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cf23545",
   "metadata": {
    "papermill": {
     "duration": 0.080262,
     "end_time": "2022-11-10T04:23:03.174096",
     "exception": false,
     "start_time": "2022-11-10T04:23:03.093834",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The data quality report was generated for Post Block Assignment 1. This section will process and select the features in accordance with the recommendations of that report. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "698d5a51",
   "metadata": {
    "papermill": {
     "duration": 0.086465,
     "end_time": "2022-11-10T04:23:03.347586",
     "exception": false,
     "start_time": "2022-11-10T04:23:03.261121",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Drop irrelevant or excess feastures\n",
    "\n",
    "The first feature to drop is 'id'. This feature is an index and not descriptive. \n",
    "\n",
    "The second feature to drop is 'attack_cat'. This feature is an extension of the target feature, therefore using it will give us 100% predictions but will not give us a generalizable model. \n",
    "\n",
    "The other features to be dropped are those that were too strongly correlated. In this current version none of them were dropped, as the model is first evaluated to see how well it can perform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "id": "dc72bef6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:03.511828Z",
     "iopub.status.busy": "2022-11-10T04:23:03.511230Z",
     "iopub.status.idle": "2022-11-10T04:23:03.515166Z",
     "shell.execute_reply": "2022-11-10T04:23:03.514356Z"
    },
    "papermill": {
     "duration": 0.086668,
     "end_time": "2022-11-10T04:23:03.517088",
     "exception": false,
     "start_time": "2022-11-10T04:23:03.430420",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "list_drop = ['id','attack_cat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "id": "933f7189",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:03.680414Z",
     "iopub.status.busy": "2022-11-10T04:23:03.679738Z",
     "iopub.status.idle": "2022-11-10T04:23:03.695130Z",
     "shell.execute_reply": "2022-11-10T04:23:03.694123Z"
    },
    "papermill": {
     "duration": 0.100966,
     "end_time": "2022-11-10T04:23:03.697695",
     "exception": false,
     "start_time": "2022-11-10T04:23:03.596729",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df.drop(list_drop,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c93649b7",
   "metadata": {
    "papermill": {
     "duration": 0.078498,
     "end_time": "2022-11-10T04:23:03.855193",
     "exception": false,
     "start_time": "2022-11-10T04:23:03.776695",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Apply Clamping\n",
    "\n",
    "The extreme values should be pruned to reduce the skewness of some distributions. The logic applied here is that the features with a maximum value more than ten times the median value is pruned to the 95th percentile. If the 95th percentile is close to the maximum, then the tail has more interesting information than what we want to discard. \n",
    "\n",
    "The clamping is also only applied to features with a maximum of more than 10 times the median. This prevents the bimodals and small value distributions from being excessively pruned.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "id": "f6c3c330",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:04.015168Z",
     "iopub.status.busy": "2022-11-10T04:23:04.014140Z",
     "iopub.status.idle": "2022-11-10T04:23:04.244071Z",
     "shell.execute_reply": "2022-11-10T04:23:04.243196Z"
    },
    "papermill": {
     "duration": 0.313565,
     "end_time": "2022-11-10T04:23:04.247080",
     "exception": false,
     "start_time": "2022-11-10T04:23:03.933515",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dur</th>\n",
       "      <th>spkts</th>\n",
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       "      <th>dwin</th>\n",
       "      <th>tcprtt</th>\n",
       "      <th>synack</th>\n",
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       "      <th>count</th>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
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       "      <td>8.233200e+04</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>8.233200e+04</td>\n",
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       "      <td>82332.000000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>8.233200e+04</td>\n",
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       "      <td>8.233200e+04</td>\n",
       "      <td>8.233200e+04</td>\n",
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       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.006756</td>\n",
       "      <td>18.666472</td>\n",
       "      <td>17.545936</td>\n",
       "      <td>7.993908e+03</td>\n",
       "      <td>1.323379e+04</td>\n",
       "      <td>8.241089e+04</td>\n",
       "      <td>180.967667</td>\n",
       "      <td>95.713003</td>\n",
       "      <td>6.454902e+07</td>\n",
       "      <td>6.305470e+05</td>\n",
       "      <td>4.753692</td>\n",
       "      <td>6.308556</td>\n",
       "      <td>755.394301</td>\n",
       "      <td>121.701284</td>\n",
       "      <td>6.363075e+03</td>\n",
       "      <td>535.180430</td>\n",
       "      <td>133.45908</td>\n",
       "      <td>1.084642e+09</td>\n",
       "      <td>1.073465e+09</td>\n",
       "      <td>128.28662</td>\n",
       "      <td>0.055925</td>\n",
       "      <td>0.029256</td>\n",
       "      <td>0.026669</td>\n",
       "      <td>139.528604</td>\n",
       "      <td>116.275069</td>\n",
       "      <td>0.094277</td>\n",
       "      <td>1.595372e+03</td>\n",
       "      <td>9.546604</td>\n",
       "      <td>1.369273</td>\n",
       "      <td>5.744923</td>\n",
       "      <td>4.928898</td>\n",
       "      <td>3.663011</td>\n",
       "      <td>7.456360</td>\n",
       "      <td>0.008284</td>\n",
       "      <td>0.008381</td>\n",
       "      <td>0.129743</td>\n",
       "      <td>6.468360</td>\n",
       "      <td>9.164262</td>\n",
       "      <td>0.011126</td>\n",
       "      <td>0.550600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.710444</td>\n",
       "      <td>133.916353</td>\n",
       "      <td>115.574086</td>\n",
       "      <td>1.716423e+05</td>\n",
       "      <td>1.514715e+05</td>\n",
       "      <td>1.486204e+05</td>\n",
       "      <td>101.513358</td>\n",
       "      <td>116.667722</td>\n",
       "      <td>1.798618e+08</td>\n",
       "      <td>2.393001e+06</td>\n",
       "      <td>64.649620</td>\n",
       "      <td>55.708021</td>\n",
       "      <td>6182.615732</td>\n",
       "      <td>1292.378499</td>\n",
       "      <td>5.672402e+04</td>\n",
       "      <td>3635.305383</td>\n",
       "      <td>127.35700</td>\n",
       "      <td>1.390860e+09</td>\n",
       "      <td>1.381996e+09</td>\n",
       "      <td>127.49137</td>\n",
       "      <td>0.116022</td>\n",
       "      <td>0.070854</td>\n",
       "      <td>0.055094</td>\n",
       "      <td>208.472063</td>\n",
       "      <td>244.600271</td>\n",
       "      <td>0.542922</td>\n",
       "      <td>3.806697e+04</td>\n",
       "      <td>11.090289</td>\n",
       "      <td>1.067188</td>\n",
       "      <td>8.418112</td>\n",
       "      <td>8.389545</td>\n",
       "      <td>5.915386</td>\n",
       "      <td>11.415191</td>\n",
       "      <td>0.091171</td>\n",
       "      <td>0.092485</td>\n",
       "      <td>0.638683</td>\n",
       "      <td>8.543927</td>\n",
       "      <td>11.121413</td>\n",
       "      <td>0.104891</td>\n",
       "      <td>0.497436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.400000e+01</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000008</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.140000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.860611e+01</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.120247e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.014138</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.340000e+02</td>\n",
       "      <td>1.780000e+02</td>\n",
       "      <td>2.650177e+03</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.770032e+05</td>\n",
       "      <td>2.112951e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.557929</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>1.762392e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>2.788886e+07</td>\n",
       "      <td>2.856975e+07</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>0.000551</td>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.719360</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.280000e+03</td>\n",
       "      <td>9.560000e+02</td>\n",
       "      <td>1.111111e+05</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>6.514286e+07</td>\n",
       "      <td>1.585808e+04</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>63.409444</td>\n",
       "      <td>63.136369</td>\n",
       "      <td>3.219332e+03</td>\n",
       "      <td>128.459914</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>2.171310e+09</td>\n",
       "      <td>2.144205e+09</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>0.105541</td>\n",
       "      <td>0.052596</td>\n",
       "      <td>0.048816</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>59.999989</td>\n",
       "      <td>10646.000000</td>\n",
       "      <td>11018.000000</td>\n",
       "      <td>1.435577e+07</td>\n",
       "      <td>1.465753e+07</td>\n",
       "      <td>1.000000e+06</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>5.268000e+09</td>\n",
       "      <td>2.082111e+07</td>\n",
       "      <td>5319.000000</td>\n",
       "      <td>5507.000000</td>\n",
       "      <td>60009.992000</td>\n",
       "      <td>57739.240000</td>\n",
       "      <td>1.483831e+06</td>\n",
       "      <td>463199.240100</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>4.294950e+09</td>\n",
       "      <td>4.294881e+09</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>3.821465</td>\n",
       "      <td>3.226788</td>\n",
       "      <td>2.928778</td>\n",
       "      <td>1504.000000</td>\n",
       "      <td>1500.000000</td>\n",
       "      <td>131.000000</td>\n",
       "      <td>5.242880e+06</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                dur         spkts         dpkts        sbytes        dbytes          rate          sttl          dttl         sload         dload         sloss         dloss        sinpkt        dinpkt          sjit           djit         swin         stcpb         dtcpb         dwin        tcprtt        synack        ackdat         smean         dmean   trans_depth  response_body_len    ct_srv_src  ct_state_ttl    ct_dst_ltm  ct_src_dport_ltm  ct_dst_sport_ltm  ct_dst_src_ltm  is_ftp_login    ct_ftp_cmd  ct_flw_http_mthd    ct_src_ltm    ct_srv_dst  is_sm_ips_ports         label\n",
       "count  82332.000000  82332.000000  82332.000000  8.233200e+04  8.233200e+04  8.233200e+04  82332.000000  82332.000000  8.233200e+04  8.233200e+04  82332.000000  82332.000000  82332.000000  82332.000000  8.233200e+04   82332.000000  82332.00000  8.233200e+04  8.233200e+04  82332.00000  82332.000000  82332.000000  82332.000000  82332.000000  82332.000000  82332.000000       8.233200e+04  82332.000000  82332.000000  82332.000000      82332.000000      82332.000000    82332.000000  82332.000000  82332.000000      82332.000000  82332.000000  82332.000000     82332.000000  82332.000000\n",
       "mean       1.006756     18.666472     17.545936  7.993908e+03  1.323379e+04  8.241089e+04    180.967667     95.713003  6.454902e+07  6.305470e+05      4.753692      6.308556    755.394301    121.701284  6.363075e+03     535.180430    133.45908  1.084642e+09  1.073465e+09    128.28662      0.055925      0.029256      0.026669    139.528604    116.275069      0.094277       1.595372e+03      9.546604      1.369273      5.744923          4.928898          3.663011        7.456360      0.008284      0.008381          0.129743      6.468360      9.164262         0.011126      0.550600\n",
       "std        4.710444    133.916353    115.574086  1.716423e+05  1.514715e+05  1.486204e+05    101.513358    116.667722  1.798618e+08  2.393001e+06     64.649620     55.708021   6182.615732   1292.378499  5.672402e+04    3635.305383    127.35700  1.390860e+09  1.381996e+09    127.49137      0.116022      0.070854      0.055094    208.472063    244.600271      0.542922       3.806697e+04     11.090289      1.067188      8.418112          8.389545          5.915386       11.415191      0.091171      0.092485          0.638683      8.543927     11.121413         0.104891      0.497436\n",
       "min        0.000000      1.000000      0.000000  2.400000e+01  0.000000e+00  0.000000e+00      0.000000      0.000000  0.000000e+00  0.000000e+00      0.000000      0.000000      0.000000      0.000000  0.000000e+00       0.000000      0.00000  0.000000e+00  0.000000e+00      0.00000      0.000000      0.000000      0.000000     24.000000      0.000000      0.000000       0.000000e+00      1.000000      0.000000      1.000000          1.000000          1.000000        1.000000      0.000000      0.000000          0.000000      1.000000      1.000000         0.000000      0.000000\n",
       "25%        0.000008      2.000000      0.000000  1.140000e+02  0.000000e+00  2.860611e+01     62.000000      0.000000  1.120247e+04  0.000000e+00      0.000000      0.000000      0.008000      0.000000  0.000000e+00       0.000000      0.00000  0.000000e+00  0.000000e+00      0.00000      0.000000      0.000000      0.000000     57.000000      0.000000      0.000000       0.000000e+00      2.000000      1.000000      1.000000          1.000000          1.000000        1.000000      0.000000      0.000000          0.000000      1.000000      2.000000         0.000000      0.000000\n",
       "50%        0.014138      6.000000      2.000000  5.340000e+02  1.780000e+02  2.650177e+03    254.000000     29.000000  5.770032e+05  2.112951e+03      1.000000      0.000000      0.557929      0.010000  1.762392e+01       0.000000    255.00000  2.788886e+07  2.856975e+07    255.00000      0.000551      0.000441      0.000080     65.000000     44.000000      0.000000       0.000000e+00      5.000000      1.000000      2.000000          1.000000          1.000000        3.000000      0.000000      0.000000          0.000000      3.000000      5.000000         0.000000      1.000000\n",
       "75%        0.719360     12.000000     10.000000  1.280000e+03  9.560000e+02  1.111111e+05    254.000000    252.000000  6.514286e+07  1.585808e+04      3.000000      2.000000     63.409444     63.136369  3.219332e+03     128.459914    255.00000  2.171310e+09  2.144205e+09    255.00000      0.105541      0.052596      0.048816    100.000000     87.000000      0.000000       0.000000e+00     11.000000      2.000000      6.000000          4.000000          3.000000        6.000000      0.000000      0.000000          0.000000      7.000000     11.000000         0.000000      1.000000\n",
       "max       59.999989  10646.000000  11018.000000  1.435577e+07  1.465753e+07  1.000000e+06    255.000000    253.000000  5.268000e+09  2.082111e+07   5319.000000   5507.000000  60009.992000  57739.240000  1.483831e+06  463199.240100    255.00000  4.294950e+09  4.294881e+09    255.00000      3.821465      3.226788      2.928778   1504.000000   1500.000000    131.000000       5.242880e+06     63.000000      6.000000     59.000000         59.000000         38.000000       63.000000      2.000000      2.000000         16.000000     60.000000     62.000000         1.000000      1.000000"
      ]
     },
     "execution_count": 460,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clamp extreme Values\n",
    "df_numeric = df.select_dtypes(include=[np.number])\n",
    "df_numeric.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "id": "81735ec8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:04.410494Z",
     "iopub.status.busy": "2022-11-10T04:23:04.409548Z",
     "iopub.status.idle": "2022-11-10T04:23:04.701218Z",
     "shell.execute_reply": "2022-11-10T04:23:04.700319Z"
    },
    "papermill": {
     "duration": 0.376488,
     "end_time": "2022-11-10T04:23:04.703949",
     "exception": false,
     "start_time": "2022-11-10T04:23:04.327461",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "DEBUG =0\n",
    "\n",
    "for feature in df_numeric.columns:\n",
    "    if DEBUG == 1:\n",
    "        print(feature)\n",
    "        print('max = '+str(df_numeric[feature].max()))\n",
    "        print('75th = '+str(df_numeric[feature].quantile(0.95)))\n",
    "        print('median = '+str(df_numeric[feature].median()))\n",
    "        print(df_numeric[feature].max()>10*df_numeric[feature].median())\n",
    "        print('----------------------------------------------------')\n",
    "    if df_numeric[feature].max()>10*df_numeric[feature].median() and df_numeric[feature].max()>10 :\n",
    "        df[feature] = np.where(df[feature]<df[feature].quantile(0.95), df[feature], df[feature].quantile(0.95))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 462,
   "id": "acfe1e69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:05.031084Z",
     "iopub.status.busy": "2022-11-10T04:23:05.030756Z",
     "iopub.status.idle": "2022-11-10T04:23:05.313165Z",
     "shell.execute_reply": "2022-11-10T04:23:05.312239Z"
    },
    "papermill": {
     "duration": 0.368854,
     "end_time": "2022-11-10T04:23:05.315761",
     "exception": false,
     "start_time": "2022-11-10T04:23:04.946907",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dur</th>\n",
       "      <th>spkts</th>\n",
       "      <th>dpkts</th>\n",
       "      <th>sbytes</th>\n",
       "      <th>dbytes</th>\n",
       "      <th>rate</th>\n",
       "      <th>sttl</th>\n",
       "      <th>dttl</th>\n",
       "      <th>sload</th>\n",
       "      <th>dload</th>\n",
       "      <th>sloss</th>\n",
       "      <th>dloss</th>\n",
       "      <th>sinpkt</th>\n",
       "      <th>dinpkt</th>\n",
       "      <th>sjit</th>\n",
       "      <th>djit</th>\n",
       "      <th>swin</th>\n",
       "      <th>stcpb</th>\n",
       "      <th>dtcpb</th>\n",
       "      <th>dwin</th>\n",
       "      <th>tcprtt</th>\n",
       "      <th>synack</th>\n",
       "      <th>ackdat</th>\n",
       "      <th>smean</th>\n",
       "      <th>dmean</th>\n",
       "      <th>trans_depth</th>\n",
       "      <th>response_body_len</th>\n",
       "      <th>ct_srv_src</th>\n",
       "      <th>ct_state_ttl</th>\n",
       "      <th>ct_dst_ltm</th>\n",
       "      <th>ct_src_dport_ltm</th>\n",
       "      <th>ct_dst_sport_ltm</th>\n",
       "      <th>ct_dst_src_ltm</th>\n",
       "      <th>is_ftp_login</th>\n",
       "      <th>ct_ftp_cmd</th>\n",
       "      <th>ct_flw_http_mthd</th>\n",
       "      <th>ct_src_ltm</th>\n",
       "      <th>ct_srv_dst</th>\n",
       "      <th>is_sm_ips_ports</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.00000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.00000</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>8.233200e+04</td>\n",
       "      <td>82332.00000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "      <td>82332.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.445016</td>\n",
       "      <td>11.84186</td>\n",
       "      <td>9.178424</td>\n",
       "      <td>1580.566135</td>\n",
       "      <td>2866.918367</td>\n",
       "      <td>71576.702810</td>\n",
       "      <td>180.967667</td>\n",
       "      <td>95.713003</td>\n",
       "      <td>4.649418e+07</td>\n",
       "      <td>3.105380e+05</td>\n",
       "      <td>2.188068</td>\n",
       "      <td>2.542729</td>\n",
       "      <td>37.836042</td>\n",
       "      <td>33.982038</td>\n",
       "      <td>1920.889858</td>\n",
       "      <td>199.566224</td>\n",
       "      <td>133.45908</td>\n",
       "      <td>1.074064e+09</td>\n",
       "      <td>1.062670e+09</td>\n",
       "      <td>128.28662</td>\n",
       "      <td>0.055925</td>\n",
       "      <td>0.029256</td>\n",
       "      <td>0.026669</td>\n",
       "      <td>124.772822</td>\n",
       "      <td>100.240891</td>\n",
       "      <td>0.092091</td>\n",
       "      <td>9.643063</td>\n",
       "      <td>9.259887</td>\n",
       "      <td>1.369273</td>\n",
       "      <td>5.269591</td>\n",
       "      <td>4.466611</td>\n",
       "      <td>3.388901</td>\n",
       "      <td>7.160679</td>\n",
       "      <td>0.008284</td>\n",
       "      <td>0.008381</td>\n",
       "      <td>0.092066</td>\n",
       "      <td>5.974809</td>\n",
       "      <td>8.832532</td>\n",
       "      <td>0.011126</td>\n",
       "      <td>0.550600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.672222</td>\n",
       "      <td>15.66461</td>\n",
       "      <td>14.504212</td>\n",
       "      <td>2948.850472</td>\n",
       "      <td>7525.606738</td>\n",
       "      <td>102631.946851</td>\n",
       "      <td>101.513358</td>\n",
       "      <td>116.667722</td>\n",
       "      <td>7.417784e+07</td>\n",
       "      <td>8.918691e+05</td>\n",
       "      <td>3.057946</td>\n",
       "      <td>4.767511</td>\n",
       "      <td>57.658385</td>\n",
       "      <td>52.184248</td>\n",
       "      <td>2900.509949</td>\n",
       "      <td>520.285264</td>\n",
       "      <td>127.35700</td>\n",
       "      <td>1.368335e+09</td>\n",
       "      <td>1.358850e+09</td>\n",
       "      <td>127.49137</td>\n",
       "      <td>0.116022</td>\n",
       "      <td>0.070854</td>\n",
       "      <td>0.055094</td>\n",
       "      <td>148.294212</td>\n",
       "      <td>184.094183</td>\n",
       "      <td>0.289156</td>\n",
       "      <td>35.977508</td>\n",
       "      <td>10.221752</td>\n",
       "      <td>1.067188</td>\n",
       "      <td>6.729755</td>\n",
       "      <td>6.685037</td>\n",
       "      <td>5.029129</td>\n",
       "      <td>10.481621</td>\n",
       "      <td>0.091171</td>\n",
       "      <td>0.092485</td>\n",
       "      <td>0.289121</td>\n",
       "      <td>6.867156</td>\n",
       "      <td>10.124902</td>\n",
       "      <td>0.104891</td>\n",
       "      <td>0.497436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000008</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>28.606114</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.120247e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.014138</td>\n",
       "      <td>6.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>534.000000</td>\n",
       "      <td>178.000000</td>\n",
       "      <td>2650.176667</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>5.770032e+05</td>\n",
       "      <td>2.112951e+03</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.557929</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>17.623918</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>2.788886e+07</td>\n",
       "      <td>2.856975e+07</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>0.000551</td>\n",
       "      <td>0.000441</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.719360</td>\n",
       "      <td>12.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1280.000000</td>\n",
       "      <td>956.000000</td>\n",
       "      <td>111111.107200</td>\n",
       "      <td>254.000000</td>\n",
       "      <td>252.000000</td>\n",
       "      <td>6.514286e+07</td>\n",
       "      <td>1.585808e+04</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>63.409444</td>\n",
       "      <td>63.136369</td>\n",
       "      <td>3219.332412</td>\n",
       "      <td>128.459914</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>2.171310e+09</td>\n",
       "      <td>2.144205e+09</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>0.105541</td>\n",
       "      <td>0.052596</td>\n",
       "      <td>0.048816</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.403792</td>\n",
       "      <td>60.00000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>12472.000000</td>\n",
       "      <td>30622.000000</td>\n",
       "      <td>333333.321500</td>\n",
       "      <td>255.000000</td>\n",
       "      <td>253.000000</td>\n",
       "      <td>2.666667e+08</td>\n",
       "      <td>3.741446e+06</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>204.530258</td>\n",
       "      <td>167.626851</td>\n",
       "      <td>9532.382646</td>\n",
       "      <td>2218.933526</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>3.876194e+09</td>\n",
       "      <td>3.862459e+09</td>\n",
       "      <td>255.00000</td>\n",
       "      <td>3.821465</td>\n",
       "      <td>3.226788</td>\n",
       "      <td>2.928778</td>\n",
       "      <td>638.000000</td>\n",
       "      <td>683.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>150.450000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                dur        spkts         dpkts        sbytes        dbytes           rate          sttl          dttl         sload         dload         sloss         dloss        sinpkt        dinpkt          sjit          djit         swin         stcpb         dtcpb         dwin        tcprtt        synack        ackdat         smean         dmean   trans_depth  response_body_len    ct_srv_src  ct_state_ttl    ct_dst_ltm  ct_src_dport_ltm  ct_dst_sport_ltm  ct_dst_src_ltm  is_ftp_login    ct_ftp_cmd  ct_flw_http_mthd    ct_src_ltm    ct_srv_dst  is_sm_ips_ports         label\n",
       "count  82332.000000  82332.00000  82332.000000  82332.000000  82332.000000   82332.000000  82332.000000  82332.000000  8.233200e+04  8.233200e+04  82332.000000  82332.000000  82332.000000  82332.000000  82332.000000  82332.000000  82332.00000  8.233200e+04  8.233200e+04  82332.00000  82332.000000  82332.000000  82332.000000  82332.000000  82332.000000  82332.000000       82332.000000  82332.000000  82332.000000  82332.000000      82332.000000      82332.000000    82332.000000  82332.000000  82332.000000      82332.000000  82332.000000  82332.000000     82332.000000  82332.000000\n",
       "mean       0.445016     11.84186      9.178424   1580.566135   2866.918367   71576.702810    180.967667     95.713003  4.649418e+07  3.105380e+05      2.188068      2.542729     37.836042     33.982038   1920.889858    199.566224    133.45908  1.074064e+09  1.062670e+09    128.28662      0.055925      0.029256      0.026669    124.772822    100.240891      0.092091           9.643063      9.259887      1.369273      5.269591          4.466611          3.388901        7.160679      0.008284      0.008381          0.092066      5.974809      8.832532         0.011126      0.550600\n",
       "std        0.672222     15.66461     14.504212   2948.850472   7525.606738  102631.946851    101.513358    116.667722  7.417784e+07  8.918691e+05      3.057946      4.767511     57.658385     52.184248   2900.509949    520.285264    127.35700  1.368335e+09  1.358850e+09    127.49137      0.116022      0.070854      0.055094    148.294212    184.094183      0.289156          35.977508     10.221752      1.067188      6.729755          6.685037          5.029129       10.481621      0.091171      0.092485          0.289121      6.867156     10.124902         0.104891      0.497436\n",
       "min        0.000000      1.00000      0.000000     24.000000      0.000000       0.000000      0.000000      0.000000  0.000000e+00  0.000000e+00      0.000000      0.000000      0.000000      0.000000      0.000000      0.000000      0.00000  0.000000e+00  0.000000e+00      0.00000      0.000000      0.000000      0.000000     24.000000      0.000000      0.000000           0.000000      1.000000      0.000000      1.000000          1.000000          1.000000        1.000000      0.000000      0.000000          0.000000      1.000000      1.000000         0.000000      0.000000\n",
       "25%        0.000008      2.00000      0.000000    114.000000      0.000000      28.606114     62.000000      0.000000  1.120247e+04  0.000000e+00      0.000000      0.000000      0.008000      0.000000      0.000000      0.000000      0.00000  0.000000e+00  0.000000e+00      0.00000      0.000000      0.000000      0.000000     57.000000      0.000000      0.000000           0.000000      2.000000      1.000000      1.000000          1.000000          1.000000        1.000000      0.000000      0.000000          0.000000      1.000000      2.000000         0.000000      0.000000\n",
       "50%        0.014138      6.00000      2.000000    534.000000    178.000000    2650.176667    254.000000     29.000000  5.770032e+05  2.112951e+03      1.000000      0.000000      0.557929      0.010000     17.623918      0.000000    255.00000  2.788886e+07  2.856975e+07    255.00000      0.000551      0.000441      0.000080     65.000000     44.000000      0.000000           0.000000      5.000000      1.000000      2.000000          1.000000          1.000000        3.000000      0.000000      0.000000          0.000000      3.000000      5.000000         0.000000      1.000000\n",
       "75%        0.719360     12.00000     10.000000   1280.000000    956.000000  111111.107200    254.000000    252.000000  6.514286e+07  1.585808e+04      3.000000      2.000000     63.409444     63.136369   3219.332412    128.459914    255.00000  2.171310e+09  2.144205e+09    255.00000      0.105541      0.052596      0.048816    100.000000     87.000000      0.000000           0.000000     11.000000      2.000000      6.000000          4.000000          3.000000        6.000000      0.000000      0.000000          0.000000      7.000000     11.000000         0.000000      1.000000\n",
       "max        2.403792     60.00000     54.000000  12472.000000  30622.000000  333333.321500    255.000000    253.000000  2.666667e+08  3.741446e+06     11.000000     18.000000    204.530258    167.626851   9532.382646   2218.933526    255.00000  3.876194e+09  3.862459e+09    255.00000      3.821465      3.226788      2.928778    638.000000    683.000000      1.000000         150.450000     37.000000      6.000000     25.000000         25.000000         18.000000       37.000000      2.000000      2.000000          1.000000     25.000000     36.000000         1.000000      1.000000"
      ]
     },
     "execution_count": 462,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_numeric = df.select_dtypes(include=[np.number])\n",
    "df_numeric.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "466cdb0f",
   "metadata": {
    "papermill": {
     "duration": 0.080105,
     "end_time": "2022-11-10T04:23:05.476666",
     "exception": false,
     "start_time": "2022-11-10T04:23:05.396561",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Apply log function to nearly all numeric, since they are all mostly skewed to the right\n",
    "\n",
    "It would have been too much of a slog to apply the log function individually, therefore a simple rule has been set up: if the number of unique values in the continuous feature is more than 50 then apply the log function. The reason more than 50 unique values are sought is to filter out the integer based features that act more categorically.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 463,
   "id": "8c64357f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:05.639995Z",
     "iopub.status.busy": "2022-11-10T04:23:05.639622Z",
     "iopub.status.idle": "2022-11-10T04:23:05.777864Z",
     "shell.execute_reply": "2022-11-10T04:23:05.776853Z"
    },
    "papermill": {
     "duration": 0.223233,
     "end_time": "2022-11-10T04:23:05.780557",
     "exception": false,
     "start_time": "2022-11-10T04:23:05.557324",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_numeric = df.select_dtypes(include=[np.number])\n",
    "df_before = df_numeric.copy()\n",
    "DEBUG = 0\n",
    "for feature in df_numeric.columns:\n",
    "    if DEBUG == 1:\n",
    "        print(feature)\n",
    "        print('nunique = '+str(df_numeric[feature].nunique()))\n",
    "        print(df_numeric[feature].nunique()>50)\n",
    "        print('----------------------------------------------------')\n",
    "    if df_numeric[feature].nunique()>50:\n",
    "        if df_numeric[feature].min()==0:\n",
    "            df[feature] = np.log(df[feature]+1)\n",
    "        else:\n",
    "            df[feature] = np.log(df[feature])\n",
    "\n",
    "df_numeric = df.select_dtypes(include=[np.number])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bff73152",
   "metadata": {
    "papermill": {
     "duration": 0.079667,
     "end_time": "2022-11-10T04:23:05.940353",
     "exception": false,
     "start_time": "2022-11-10T04:23:05.860686",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Reduce the labels in catagorical features\n",
    "\n",
    "Some features have very high cardinalities, and this section reduces the cardinality to 5 or 6 per feature. The logic is to take the top 5 occuring labels in the feature as the labels and set the remainder to '-' (seldom used) labels. When the encoding is done later on, the dimensionality will not explode and cause the curse of dimensionality. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "id": "1174af4e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:06.103509Z",
     "iopub.status.busy": "2022-11-10T04:23:06.103185Z",
     "iopub.status.idle": "2022-11-10T04:23:06.150409Z",
     "shell.execute_reply": "2022-11-10T04:23:06.149206Z"
    },
    "papermill": {
     "duration": 0.131144,
     "end_time": "2022-11-10T04:23:06.152724",
     "exception": false,
     "start_time": "2022-11-10T04:23:06.021580",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>proto</th>\n",
       "      <th>service</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>82332</td>\n",
       "      <td>82332</td>\n",
       "      <td>82332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>131</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>tcp</td>\n",
       "      <td>-</td>\n",
       "      <td>FIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>43095</td>\n",
       "      <td>47153</td>\n",
       "      <td>39339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        proto service  state\n",
       "count   82332   82332  82332\n",
       "unique    131      13      7\n",
       "top       tcp       -    FIN\n",
       "freq    43095   47153  39339"
      ]
     },
     "execution_count": 464,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat = df.select_dtypes(exclude=[np.number])\n",
    "df_cat.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "id": "6563c525",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:06.335893Z",
     "iopub.status.busy": "2022-11-10T04:23:06.335123Z",
     "iopub.status.idle": "2022-11-10T04:23:06.403291Z",
     "shell.execute_reply": "2022-11-10T04:23:06.402368Z"
    },
    "papermill": {
     "duration": 0.172146,
     "end_time": "2022-11-10T04:23:06.406052",
     "exception": false,
     "start_time": "2022-11-10T04:23:06.233906",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "DEBUG = 0\n",
    "for feature in df_cat.columns:\n",
    "    if DEBUG == 1:\n",
    "        print(feature)\n",
    "        print('nunique = '+str(df_cat[feature].nunique()))\n",
    "        print(df_cat[feature].nunique()>6)\n",
    "        print(sum(df[feature].isin(df[feature].value_counts().head().index)))\n",
    "        print('----------------------------------------------------')\n",
    "    \n",
    "    if df_cat[feature].nunique()>6:\n",
    "        df[feature] = np.where(df[feature].isin(df[feature].value_counts().head().index), df[feature], '-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "id": "a795a9d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:06.572768Z",
     "iopub.status.busy": "2022-11-10T04:23:06.572461Z",
     "iopub.status.idle": "2022-11-10T04:23:06.620987Z",
     "shell.execute_reply": "2022-11-10T04:23:06.620003Z"
    },
    "papermill": {
     "duration": 0.135325,
     "end_time": "2022-11-10T04:23:06.623449",
     "exception": false,
     "start_time": "2022-11-10T04:23:06.488124",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>proto</th>\n",
       "      <th>service</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>82332</td>\n",
       "      <td>82332</td>\n",
       "      <td>82332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>tcp</td>\n",
       "      <td>-</td>\n",
       "      <td>FIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>43095</td>\n",
       "      <td>49275</td>\n",
       "      <td>39339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "        proto service  state\n",
       "count   82332   82332  82332\n",
       "unique      6       5      6\n",
       "top       tcp       -    FIN\n",
       "freq    43095   49275  39339"
      ]
     },
     "execution_count": 466,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat = df.select_dtypes(exclude=[np.number])\n",
    "df_cat.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "id": "a37b0eeb",
   "metadata": {
    "execution": {
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     "exception": false,
     "start_time": "2022-11-10T04:23:06.708217",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tcp', 'udp', '-', 'unas', 'arp'], dtype='object', name='proto')"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['proto'].value_counts().head().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "id": "4c1991b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:06.976062Z",
     "iopub.status.busy": "2022-11-10T04:23:06.975731Z",
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     "shell.execute_reply": "2022-11-10T04:23:06.988112Z"
    },
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     "end_time": "2022-11-10T04:23:06.991203",
     "exception": false,
     "start_time": "2022-11-10T04:23:06.891845",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tcp', 'udp', '-', 'unas', 'arp', 'ospf'], dtype='object', name='proto')"
      ]
     },
     "execution_count": 468,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['proto'].value_counts().index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bba9f4a5",
   "metadata": {
    "papermill": {
     "duration": 0.082878,
     "end_time": "2022-11-10T04:23:07.157324",
     "exception": false,
     "start_time": "2022-11-10T04:23:07.074446",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## View before and after of features\n",
    "\n",
    "This section simply displays the distributions within features before and after the transformations.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4abdd71c",
   "metadata": {
    "papermill": {
     "duration": 0.082823,
     "end_time": "2022-11-10T04:23:07.322193",
     "exception": false,
     "start_time": "2022-11-10T04:23:07.239370",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Best Features\n",
    "\n",
    "This section does an analysis (univariate statistical tests) to determine which features best predict the target feature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "id": "7c765698",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import plotly.offline as py\n",
    "# py.init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "id": "99cdafb8",
   "metadata": {
    "execution": {
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     "start_time": "2022-11-10T04:23:07.404336",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "            Plotly.purge(gd);\n",
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       "\n",
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       "if (notebookContainer) {{\n",
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      ]
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     "metadata": {},
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   ],
   "source": [
    "# Feature Selection\n",
    "from sklearn.feature_selection import SelectKBest, chi2\n",
    "\n",
    "best_features = SelectKBest(score_func=chi2,k='all')\n",
    "\n",
    "X = df.iloc[:,4:-2]\n",
    "y = df.iloc[:,-1]\n",
    "fit = best_features.fit(X,y)\n",
    "\n",
    "df_scores=pd.DataFrame(fit.scores_)\n",
    "df_col=pd.DataFrame(X.columns)\n",
    "\n",
    "feature_score=pd.concat([df_col,df_scores],axis=1)\n",
    "feature_score.columns=['feature','score']\n",
    "feature_score.sort_values(by=['score'],ascending=True,inplace=True)\n",
    "\n",
    "fig = go.Figure(go.Bar(\n",
    "            x=feature_score['score'][0:21],\n",
    "            y=feature_score['feature'][0:21],\n",
    "            orientation='h'))\n",
    "\n",
    "# fig.update_layout(title=\"Top 20 Features\",\n",
    "#                   height=1200,\n",
    "#                   showlegend=False,\n",
    "#                  )\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daef956c",
   "metadata": {
    "papermill": {
     "duration": 0.08194,
     "end_time": "2022-11-10T04:23:08.196513",
     "exception": false,
     "start_time": "2022-11-10T04:23:08.114573",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Encode categorical features\n",
    "\n",
    "The categorical features must be encoded to ensure that the models can interpret them. One-hot encoding is used since none of the categorical features are ordinal.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "id": "886d6ef8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:08.365324Z",
     "iopub.status.busy": "2022-11-10T04:23:08.364672Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X = df.iloc[:,:-1]\n",
    "y = df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "id": "607a04e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:08.554701Z",
     "iopub.status.busy": "2022-11-10T04:23:08.554123Z",
     "iopub.status.idle": "2022-11-10T04:23:08.559759Z",
     "shell.execute_reply": "2022-11-10T04:23:08.559138Z"
    },
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     "duration": 0.093165,
     "end_time": "2022-11-10T04:23:08.561556",
     "exception": false,
     "start_time": "2022-11-10T04:23:08.468391",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(82332, 42)"
      ]
     },
     "execution_count": 472,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()\n",
    "feature_names = list(X.columns)\n",
    "np.shape(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "id": "e6e140f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:08.733193Z",
     "iopub.status.busy": "2022-11-10T04:23:08.732879Z",
     "iopub.status.idle": "2022-11-10T04:23:08.898084Z",
     "shell.execute_reply": "2022-11-10T04:23:08.897306Z"
    },
    "papermill": {
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     "end_time": "2022-11-10T04:23:08.900526",
     "exception": false,
     "start_time": "2022-11-10T04:23:08.646802",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [1,2,3])], remainder='passthrough')\n",
    "X = np.array(ct.fit_transform(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "id": "92060b30",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:09.067319Z",
     "iopub.status.busy": "2022-11-10T04:23:09.066980Z",
     "iopub.status.idle": "2022-11-10T04:23:09.072701Z",
     "shell.execute_reply": "2022-11-10T04:23:09.072132Z"
    },
    "papermill": {
     "duration": 0.091444,
     "end_time": "2022-11-10T04:23:09.074500",
     "exception": false,
     "start_time": "2022-11-10T04:23:08.983056",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(82332, 56)"
      ]
     },
     "execution_count": 474,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "id": "0031897d",
   "metadata": {
    "execution": {
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   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>proto</th>\n",
       "      <th>service</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
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       "      <td>82332</td>\n",
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       "      <th>unique</th>\n",
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       "      <td>6</td>\n",
       "    </tr>\n",
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       "      <th>top</th>\n",
       "      <td>tcp</td>\n",
       "      <td>-</td>\n",
       "      <td>FIN</td>\n",
       "    </tr>\n",
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       "      <th>freq</th>\n",
       "      <td>43095</td>\n",
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       "        proto service  state\n",
       "count   82332   82332  82332\n",
       "unique      6       5      6\n",
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       "freq    43095   49275  39339"
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    "df_cat.describe(include='all')"
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    {
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       "array([0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
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       "       0.00000000e+00, 1.00000000e+00, 2.00000000e+00, 0.00000000e+00])"
      ]
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   "source": [
    "X[0]"
   ]
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   "id": "b20fec01",
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    {
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       "42"
      ]
     },
     "execution_count": 477,
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   ],
   "source": [
    "len(feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "id": "b5409918",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "for label in list(df_cat['state'].value_counts().index)[::-1][1:]:\n",
    "    feature_names.insert(0,label)\n",
    "    \n",
    "for label in list(df_cat['service'].value_counts().index)[::-1][1:]:\n",
    "    feature_names.insert(0,label)\n",
    "    \n",
    "for label in list(df_cat['proto'].value_counts().index)[::-1][1:]:\n",
    "    feature_names.insert(0,label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "id": "86b45a67",
   "metadata": {
    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(feature_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c343bd06",
   "metadata": {
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     "end_time": "2022-11-10T04:23:10.184329",
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   "source": [
    "<a id='4'></a>\n",
    "# <p style=\"padding: 8px;color:white; display:fill;background-color:#555555; border-radius:5px; font-size:100%\"> <b>Modelling and Evaluation</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4970d5f",
   "metadata": {
    "papermill": {
     "duration": 0.083795,
     "end_time": "2022-11-10T04:23:10.352675",
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     "start_time": "2022-11-10T04:23:10.268880",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Prep for Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a36ec28f",
   "metadata": {
    "papermill": {
     "duration": 0.083593,
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     "start_time": "2022-11-10T04:23:10.436585",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Split test and training\n",
    "In this section the data is split into test and training sets using stratified sampling. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "id": "675d8acf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:10.693492Z",
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     "shell.execute_reply": "2022-11-10T04:23:10.739989Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size = 0.2, \n",
    "                                                    random_state = 0,\n",
    "                                                    stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e85a589",
   "metadata": {
    "papermill": {
     "duration": 0.084056,
     "end_time": "2022-11-10T04:23:10.912362",
     "exception": false,
     "start_time": "2022-11-10T04:23:10.828306",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Standardize continuous features\n",
    "a standard scaler is used on the continuous features to put them all in the same order of size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "id": "2e105f34",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2022-11-10T04:23:11.085010Z",
     "iopub.status.idle": "2022-11-10T04:23:11.126973Z",
     "shell.execute_reply": "2022-11-10T04:23:11.125806Z"
    },
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     "duration": 0.130642,
     "end_time": "2022-11-10T04:23:11.129145",
     "exception": false,
     "start_time": "2022-11-10T04:23:10.998503",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>proto</th>\n",
       "      <th>service</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>82332</td>\n",
       "      <td>82332</td>\n",
       "      <td>82332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>tcp</td>\n",
       "      <td>-</td>\n",
       "      <td>FIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>43095</td>\n",
       "      <td>49275</td>\n",
       "      <td>39339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        proto service  state\n",
       "count   82332   82332  82332\n",
       "unique      6       5      6\n",
       "top       tcp       -    FIN\n",
       "freq    43095   49275  39339"
      ]
     },
     "execution_count": 481,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "id": "31d553a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "164664"
      ]
     },
     "execution_count": 482,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "82332*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "id": "85a84460",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:11.301177Z",
     "iopub.status.busy": "2022-11-10T04:23:11.300810Z",
     "iopub.status.idle": "2022-11-10T04:23:11.372055Z",
     "shell.execute_reply": "2022-11-10T04:23:11.371228Z"
    },
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     "end_time": "2022-11-10T04:23:11.374419",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# 6 + 5 + 6 unique = 17, therefore the first 17 rows will be the categories that have been encoded, start scaling from row 18 only.\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train[:, 18:] = sc.fit_transform(X_train[:, 18:])\n",
    "X_test[:, 18:] = sc.transform(X_test[:, 18:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8fb7498",
   "metadata": {
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     "duration": 0.085637,
     "end_time": "2022-11-10T04:23:11.544884",
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     "start_time": "2022-11-10T04:23:11.459247",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Import Metrics\n",
    "\n",
    "Imports the libraries that will be used to evaluate the models later on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 484,
   "id": "8d86e59a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:11.721025Z",
     "iopub.status.busy": "2022-11-10T04:23:11.720513Z",
     "iopub.status.idle": "2022-11-10T04:23:11.727410Z",
     "shell.execute_reply": "2022-11-10T04:23:11.726718Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import f1_score, precision_score, recall_score, accuracy_score\n",
    "from sklearn.metrics import ConfusionMatrixDisplay # will plot the confusion matrix\n",
    "\n",
    "import time\n",
    "model_performance = pd.DataFrame(columns=['Accuracy','Recall','Precision','F1-Score','time to train','time to predict','total time'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01afdc9b",
   "metadata": {
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "<a id='4_1'></a>\n",
    "## <p style=\"padding: 8px;color:white; display:fill;background-color:#aaaaaa; border-radius:5px; font-size:100%\"> <b>Logistical Classification</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "id": "6928748f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:12.078238Z",
     "iopub.status.busy": "2022-11-10T04:23:12.077852Z",
     "iopub.status.idle": "2022-11-10T04:23:13.771012Z",
     "shell.execute_reply": "2022-11-10T04:23:13.769763Z"
    },
    "papermill": {
     "duration": 1.786461,
     "end_time": "2022-11-10T04:23:13.775980",
     "exception": false,
     "start_time": "2022-11-10T04:23:11.989519",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 625 ms\n",
      "Wall time: 619 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "start = time.time()\n",
    "model = LogisticRegression().fit(X_train,y_train)\n",
    "end_train = time.time()\n",
    "y_predictions = model.predict(X_test) # These are the predictions from the test data.\n",
    "end_predict = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "id": "1cbcc9ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:14.016755Z",
     "iopub.status.busy": "2022-11-10T04:23:14.016408Z",
     "iopub.status.idle": "2022-11-10T04:23:14.056007Z",
     "shell.execute_reply": "2022-11-10T04:23:14.055115Z"
    },
    "papermill": {
     "duration": 0.129862,
     "end_time": "2022-11-10T04:23:14.059340",
     "exception": false,
     "start_time": "2022-11-10T04:23:13.929478",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 92.83%\n",
      "Recall: 92.83%\n",
      "Precision: 92.87%\n",
      "F1-Score: 92.84%\n",
      "time to train: 0.62 s\n",
      "time to predict: 0.00 s\n",
      "total: 0.62 s\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_predictions)\n",
    "recall = recall_score(y_test, y_predictions, average='weighted')\n",
    "precision = precision_score(y_test, y_predictions, average='weighted')\n",
    "f1s = f1_score(y_test, y_predictions, average='weighted')\n",
    "\n",
    "print(\"Accuracy: \"+ \"{:.2%}\".format(accuracy))\n",
    "print(\"Recall: \"+ \"{:.2%}\".format(recall))\n",
    "print(\"Precision: \"+ \"{:.2%}\".format(precision))\n",
    "print(\"F1-Score: \"+ \"{:.2%}\".format(f1s))\n",
    "print(\"time to train: \"+ \"{:.2f}\".format(end_train-start)+\" s\")\n",
    "print(\"time to predict: \"+\"{:.2f}\".format(end_predict-end_train)+\" s\")\n",
    "print(\"total: \"+\"{:.2f}\".format(end_predict-start)+\" s\")\n",
    "model_performance.loc['Logistic'] = [accuracy, recall, precision, f1s,end_train-start,end_predict-end_train,end_predict-start]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "id": "d03b49fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:14.235668Z",
     "iopub.status.busy": "2022-11-10T04:23:14.235366Z",
     "iopub.status.idle": "2022-11-10T04:23:14.626710Z",
     "shell.execute_reply": "2022-11-10T04:23:14.625698Z"
    },
    "papermill": {
     "duration": 0.482977,
     "end_time": "2022-11-10T04:23:14.629221",
     "exception": false,
     "start_time": "2022-11-10T04:23:14.146244",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 408,
       "width": 445
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=5,5 \n",
    "sns.set_style(\"white\")\n",
    "ConfusionMatrixDisplay.from_predictions(y_test,y_predictions, cmap=plt.cm.Blues)  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "959f2d0b",
   "metadata": {
    "papermill": {
     "duration": 0.086269,
     "end_time": "2022-11-10T04:23:14.804747",
     "exception": false,
     "start_time": "2022-11-10T04:23:14.718478",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id='4_2'></a>\n",
    "## <p style=\"padding: 8px;color:white; display:fill;background-color:#aaaaaa; border-radius:5px; font-size:100%\"> <b>kNN</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "id": "ab0c2fd4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:14.979870Z",
     "iopub.status.busy": "2022-11-10T04:23:14.979547Z",
     "iopub.status.idle": "2022-11-10T04:23:35.034998Z",
     "shell.execute_reply": "2022-11-10T04:23:35.033100Z"
    },
    "papermill": {
     "duration": 20.146984,
     "end_time": "2022-11-10T04:23:35.038490",
     "exception": false,
     "start_time": "2022-11-10T04:23:14.891506",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 8.92 s\n",
      "Wall time: 1.14 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "start = time.time()\n",
    "model = KNeighborsClassifier(n_neighbors=3).fit(X_train,y_train)\n",
    "end_train = time.time()\n",
    "y_predictions = model.predict(X_test) # These are the predictions from the test data.\n",
    "end_predict = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "id": "3ba8215a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:35.249816Z",
     "iopub.status.busy": "2022-11-10T04:23:35.248786Z",
     "iopub.status.idle": "2022-11-10T04:23:35.292357Z",
     "shell.execute_reply": "2022-11-10T04:23:35.291305Z"
    },
    "papermill": {
     "duration": 0.14973,
     "end_time": "2022-11-10T04:23:35.295383",
     "exception": false,
     "start_time": "2022-11-10T04:23:35.145653",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 95.04%\n",
      "Recall: 95.04%\n",
      "Precision: 95.09%\n",
      "F1-Score: 95.05%\n",
      "time to train: 0.01 s\n",
      "time to predict: 1.14 s\n",
      "total: 1.14 s\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_predictions)\n",
    "recall = recall_score(y_test, y_predictions, average='weighted')\n",
    "precision = precision_score(y_test, y_predictions, average='weighted')\n",
    "f1s = f1_score(y_test, y_predictions, average='weighted')\n",
    "\n",
    "print(\"Accuracy: \"+ \"{:.2%}\".format(accuracy))\n",
    "print(\"Recall: \"+ \"{:.2%}\".format(recall))\n",
    "print(\"Precision: \"+ \"{:.2%}\".format(precision))\n",
    "print(\"F1-Score: \"+ \"{:.2%}\".format(f1s))\n",
    "print(\"time to train: \"+ \"{:.2f}\".format(end_train-start)+\" s\")\n",
    "print(\"time to predict: \"+\"{:.2f}\".format(end_predict-end_train)+\" s\")\n",
    "print(\"total: \"+\"{:.2f}\".format(end_predict-start)+\" s\")\n",
    "model_performance.loc['kNN'] = [accuracy, recall, precision, f1s,end_train-start,end_predict-end_train,end_predict-start]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "id": "ea0ca032",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:35.485121Z",
     "iopub.status.busy": "2022-11-10T04:23:35.484546Z",
     "iopub.status.idle": "2022-11-10T04:23:55.031140Z",
     "shell.execute_reply": "2022-11-10T04:23:55.030388Z"
    },
    "papermill": {
     "duration": 19.644765,
     "end_time": "2022-11-10T04:23:55.033423",
     "exception": false,
     "start_time": "2022-11-10T04:23:35.388658",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 408,
       "width": 445
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=5,5 \n",
    "sns.set_style(\"white\")\n",
    "ConfusionMatrixDisplay.from_predictions(y_test,y_predictions, cmap=plt.cm.Blues) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f7a9f51",
   "metadata": {
    "papermill": {
     "duration": 0.091324,
     "end_time": "2022-11-10T04:23:55.217311",
     "exception": false,
     "start_time": "2022-11-10T04:23:55.125987",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id='4_3'></a>\n",
    "## <p style=\"padding: 8px;color:white; display:fill;background-color:#aaaaaa; border-radius:5px; font-size:100%\"> <b>Decision Tree</b>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "id": "8a441ab6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:55.408195Z",
     "iopub.status.busy": "2022-11-10T04:23:55.407809Z",
     "iopub.status.idle": "2022-11-10T04:23:56.827646Z",
     "shell.execute_reply": "2022-11-10T04:23:56.826584Z"
    },
    "papermill": {
     "duration": 1.514954,
     "end_time": "2022-11-10T04:23:56.829996",
     "exception": false,
     "start_time": "2022-11-10T04:23:55.315042",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 875 ms\n",
      "Wall time: 874 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "start = time.time()\n",
    "model = DecisionTreeClassifier().fit(X_train,y_train)\n",
    "end_train = time.time()\n",
    "y_predictions = model.predict(X_test) # These are the predictions from the test data.\n",
    "end_predict = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "id": "f04c2704",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:57.013611Z",
     "iopub.status.busy": "2022-11-10T04:23:57.013244Z",
     "iopub.status.idle": "2022-11-10T04:23:57.052677Z",
     "shell.execute_reply": "2022-11-10T04:23:57.051736Z"
    },
    "papermill": {
     "duration": 0.134682,
     "end_time": "2022-11-10T04:23:57.055213",
     "exception": false,
     "start_time": "2022-11-10T04:23:56.920531",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 96.45%\n",
      "Recall: 96.45%\n",
      "Precision: 96.45%\n",
      "F1-Score: 96.45%\n",
      "time to train: 0.87 s\n",
      "time to predict: 0.00 s\n",
      "total: 0.87 s\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_predictions)\n",
    "recall = recall_score(y_test, y_predictions, average='weighted')\n",
    "precision = precision_score(y_test, y_predictions, average='weighted')\n",
    "f1s = f1_score(y_test, y_predictions, average='weighted')\n",
    "\n",
    "print(\"Accuracy: \"+ \"{:.2%}\".format(accuracy))\n",
    "print(\"Recall: \"+ \"{:.2%}\".format(recall))\n",
    "print(\"Precision: \"+ \"{:.2%}\".format(precision))\n",
    "print(\"F1-Score: \"+ \"{:.2%}\".format(f1s))\n",
    "print(\"time to train: \"+ \"{:.2f}\".format(end_train-start)+\" s\")\n",
    "print(\"time to predict: \"+\"{:.2f}\".format(end_predict-end_train)+\" s\")\n",
    "print(\"total: \"+\"{:.2f}\".format(end_predict-start)+\" s\")\n",
    "model_performance.loc['Decision Tree'] = [accuracy, recall, precision, f1s,end_train-start,end_predict-end_train,end_predict-start]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "id": "e5382459",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:57.241231Z",
     "iopub.status.busy": "2022-11-10T04:23:57.240910Z",
     "iopub.status.idle": "2022-11-10T04:23:57.536539Z",
     "shell.execute_reply": "2022-11-10T04:23:57.535509Z"
    },
    "papermill": {
     "duration": 0.391089,
     "end_time": "2022-11-10T04:23:57.538896",
     "exception": false,
     "start_time": "2022-11-10T04:23:57.147807",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 408,
       "width": 445
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=5,5 \n",
    "sns.set_style(\"white\")\n",
    "ConfusionMatrixDisplay.from_predictions(y_test,y_predictions, cmap=plt.cm.Blues) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "id": "dabad9e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:57.733688Z",
     "iopub.status.busy": "2022-11-10T04:23:57.733395Z",
     "iopub.status.idle": "2022-11-10T04:23:58.196549Z",
     "shell.execute_reply": "2022-11-10T04:23:58.195662Z"
    },
    "papermill": {
     "duration": 0.567563,
     "end_time": "2022-11-10T04:23:58.199402",
     "exception": false,
     "start_time": "2022-11-10T04:23:57.631839",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 812,
       "width": 931
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=10,10\n",
    "sns.set_style(\"white\")\n",
    "feat_importances = pd.Series(model.feature_importances_, index=feature_names)\n",
    "feat_importances = feat_importances.groupby(level=0).mean()\n",
    "feat_importances.nlargest(20).plot(kind='barh').invert_yaxis()\n",
    "sns.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f84a702",
   "metadata": {
    "papermill": {
     "duration": 0.09576,
     "end_time": "2022-11-10T04:23:58.389342",
     "exception": false,
     "start_time": "2022-11-10T04:23:58.293582",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id='4_4'></a>\n",
    "## <p style=\"padding: 8px;color:white; display:fill;background-color:#aaaaaa; border-radius:5px; font-size:100%\"> <b>Extra Trees</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "id": "ee07214a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:23:58.581365Z",
     "iopub.status.busy": "2022-11-10T04:23:58.580813Z",
     "iopub.status.idle": "2022-11-10T04:24:02.530009Z",
     "shell.execute_reply": "2022-11-10T04:24:02.528723Z"
    },
    "papermill": {
     "duration": 4.049858,
     "end_time": "2022-11-10T04:24:02.533527",
     "exception": false,
     "start_time": "2022-11-10T04:23:58.483669",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 15.9 s\n",
      "Wall time: 1.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "start = time.time()\n",
    "model = ExtraTreesClassifier(random_state=0,n_jobs=-1).fit(X_train,y_train)\n",
    "end_train = time.time()\n",
    "y_predictions = model.predict(X_test) # These are the predictions from the test data.\n",
    "end_predict = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "id": "a2a3f1e8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:24:02.728586Z",
     "iopub.status.busy": "2022-11-10T04:24:02.728274Z",
     "iopub.status.idle": "2022-11-10T04:24:02.774202Z",
     "shell.execute_reply": "2022-11-10T04:24:02.772949Z"
    },
    "papermill": {
     "duration": 0.147789,
     "end_time": "2022-11-10T04:24:02.776970",
     "exception": false,
     "start_time": "2022-11-10T04:24:02.629181",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 97.53%\n",
      "Recall: 97.53%\n",
      "Precision: 97.55%\n",
      "F1-Score: 97.53%\n",
      "time to train: 1.25 s\n",
      "time to predict: 0.05 s\n",
      "total: 1.30 s\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_predictions)\n",
    "recall = recall_score(y_test, y_predictions, average='weighted')\n",
    "precision = precision_score(y_test, y_predictions, average='weighted')\n",
    "f1s = f1_score(y_test, y_predictions, average='weighted')\n",
    "\n",
    "print(\"Accuracy: \"+ \"{:.2%}\".format(accuracy))\n",
    "print(\"Recall: \"+ \"{:.2%}\".format(recall))\n",
    "print(\"Precision: \"+ \"{:.2%}\".format(precision))\n",
    "print(\"F1-Score: \"+ \"{:.2%}\".format(f1s))\n",
    "print(\"time to train: \"+ \"{:.2f}\".format(end_train-start)+\" s\")\n",
    "print(\"time to predict: \"+\"{:.2f}\".format(end_predict-end_train)+\" s\")\n",
    "print(\"total: \"+\"{:.2f}\".format(end_predict-start)+\" s\")\n",
    "model_performance.loc['Extra Trees'] = [accuracy, recall, precision, f1s,end_train-start,end_predict-end_train,end_predict-start]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "id": "176a7350",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:24:02.971035Z",
     "iopub.status.busy": "2022-11-10T04:24:02.970702Z",
     "iopub.status.idle": "2022-11-10T04:24:03.499026Z",
     "shell.execute_reply": "2022-11-10T04:24:03.498090Z"
    },
    "papermill": {
     "duration": 0.627776,
     "end_time": "2022-11-10T04:24:03.501788",
     "exception": false,
     "start_time": "2022-11-10T04:24:02.874012",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AABZDogcAAAAAFkPrJgAAAOBHaN1EVVDRAwAAAACLIdEDAAAAAIuhdRMAAADwJ8avLzMwyzrghooeAAAAAFgMiR4AAAAAWAytmwAAAIAfYddNVAUVPQAAAACwGBI9AAAAALAYWjcBAAAAP0LrJqqCih4AAAAAWAyJHgAAAABYDK2bAAAAgB+hdRNVQUUPAAAAACyGRA8AAAAALIbWTQAAAMCPGDJPy6Q5VoGyUNEDAAAAAIsh0QMAAAAAi6F1EwAAAPAnhszTM2mWdcANFT0AAAAAsBgSPQAAAACwGFo3AQAAAD/CgemoCip6AAAAAGAxJHoAAAAAYDG0bgIAAAD+xEStmzLLOuCGih4AAAAAWAyJHgAAAABYDK2bAAAAgB8xDMM0LZOmaSGFGyp6AAAAAGAxJHoAAAAAYDG0bgIAAAD+xPj1ZQZmWQfcUNEDAAAAAIuhogcAAADAdEaNGqUPPvjgrObMnTtX1157rev3drtdCxYs0PLly5WamiqHw6GoqCj16NFDCQkJCgsLq/B+e/fu1fTp07VlyxZlZWUpLCxM7du3V3x8vLp06VLhXE9je4pEDwAAAPAj7LpZvpCQENf7/Px8DRkyRFu3bi01Zt++fdq3b5+WLFmiGTNmKCYmpsx7rVu3TiNHjpTdbnd9lpmZqQ0bNmjDhg26//77NWbMmDLnehrbG2jdBAAAAGA6zz//vL7++usKX2+++aYr2XzooYfUvn171/zRo0dr69atCgoK0uOPP65169Zp06ZNGjdunEJDQ3X06FENHz5cp0+fdoudkpKiJ554Qna7XbGxsUpKStKXX36pxYsXq0ePHpKkpKQkzZs3r8y1exLbW0j0AAAAAJhOjRo1FBISUu7r1KlTGjNmjBwOhzp16qTHH3/cNXfHjh1auXKlJOnZZ5/V8OHDFR0drcjISPXr10+zZ89WUFCQ0tPTNXfuXLfYb7zxhvLy8tS8eXPNmTNHnTp1Unh4uGJjYzV16lT17NlTkjR58mTl5uaWmutpbG8h0QMAAAD8iGEYpnpVlzFjxuj48eMKCQnRxIkTZbP9ltrMmjVLkhQdHa3+/fu7zW3btq169+4tSVq0aFGpa6mpqUpOTpYkDRs2rFQ7qOT8+o8aNUo2m03Z2dlas2ZNqeuexPYmEj0AAAAAfuWjjz7Sxo0bJUlPPvmkmjRp4rrmcDi0adMmSVJcXJwCAgLKvEf37t0lSWlpadqzZ4/r8+K5hmEoLi6uzLlNmjRRmzZtJElr1671WmxvItEDAAAA4Dfy8/P18ssvS5JiYmJ0zz33lLqelpamnJwcSVK7du3KvU/btm1d73fu3Ol6v3v3bklS06ZNFRERUen8Xbt2eS22N7HrJgAAAOBHzLrr5tGjRysd37hxY49jzp8/X4cOHZIkPfXUU25Vs/T0dNf76Ojocu/TsGFDBQUFyW63Ky0tzW1+RXMlZyIoSRkZGSosLFRgYKDHsb2JRA8AAACAx/r161fpmL1793oUo7CwULNnz5bkrIp17drVbczx48dd7+vVq1fuvWw2m0JCQpSdne2qwpWcHxoaWuFa6tatK8nZrpmTk6OIiAiPY3sTrZsAAAAA/MJHH32kjIwMSc7jFMqSn5/vel+rVq0K71ezZk23OcXvi6+Vp+S9CwoKvBLbm6joAQAAAH7EkIlaN/XbOhYtWqTIyEifxiuu5rVo0UK33HJLmWPK2wClqjyZ72lsbyLRAwAAAOCxyMhIrzyDV54ff/zRtfFJ7969Sx2nUFLt2rVd7yurlhVfL1l9K55fXKUrT15enut9cXXO09jeROsmAAAAANNbvXq16/1tt91W7riSz8adPHmy3HFFRUU6deqUJCk8PNz1efGzdxXNleR6ti4gIMD1PJ+nsb2JRA8AAADwJ4bJXudJcaIXGxuriy66qNxxLVq0cL0v3p2zLJmZmbLb7ZJU6hy+li1bVjpXkg4fPixJatSokau66GlsbyLRAwAAAGBqWVlZrrbNm266qcKxkZGRCgsLkySlpKSUO67k+Xclz7WLiYmRJB08eFC5ubnlzi++d/HB6d6I7U0kegAAAABM7euvv5bD4ZAkdejQodLxxccuJCcnu+b93vr16yU5z7Rr3bq129wzZ84oOTm5zLmHDx92HazeuXNnr8X2JhI9AAAAwI8YhmGq1/lQXAEzDEOxsbGVju/Tp48kaf/+/Zo/f77b9ZSUFC1dulSSNGjQoFJ/jmbNmqljx46SpClTprg9a+dwODRhwgQVFRUpPDxcvXr18lpsbyLRAwAAAGBqqampkqT69eurTp06lY6//vrr1a1bN0nS+PHjNWnSJB08eFCZmZlavHixEhISZLfbFR0drQEDBrjNHz16tGw2mw4cOKD4+Hht3rzZ1T6amJioVatWSZISExMVHBzs1djewvEKAAAAAEwtPT1d0m87YlbFhAkTNHjwYO3YsUPTpk3TtGnTSl1v0KCBZs6cWWbiGBsbq/Hjx2vs2LH6/vvvNXjwYLcxCQkJuvfee70e21tI9AAAAAA/YhjF/6f6na9lFLdPljy+oDKhoaFasGCBFixYoBUrVig1NVUFBQWKiopSXFychg4dqvr165c7v2/fvmrXrp1mzJihLVu26NixYwoODlb79u0VHx+vHj16+Cy2N5DoAQAAADC1kmfonY2goCANHDhQAwcOPKf5rVq10sSJE6sltqd4Rg8AAAAALIaKHgAAAOBHDMMwUeumOdYBd1T0AAAAAMBiSPQAAAAAwGJo3QQAAAD8ifHrywzMsg64oaIHAAAAABZDogcAAAAAFkPrJgAAAOBPDMM8u12aZR1wQ0UPAAAAACyGRA8AAAAALIbWTQAAAMCPmOnAdNOsA26o6AEAAACAxZDoAQAAAIDF0LoJAAAA+BFaN1EVVPQAAAAAwGJI9AAAAADAYmjdBAAAAPyIIRO1bsos68DvUdEDAAAAAIsh0QMAAAAAi6F1EwAAAPAnhszTMWmWdcANFT0AAAAAsBgSPQAAAACwGFo3AQAAAD/CgemoCip6AAAAAGAxJHoAAAAAYDG0bgIAAAB+hNZNVAUVPQAAAACwGBI9AAAAALAYWjcBAAAAP2JwYDqqgIoeAAAAAFgMiR4AAAAAWAytmwAAAIAfYddNVAUVPQAAAACwGBI9AAAAALAYWjcBAAAAP8Kum6gKKnoAAAAAYDEkegAAAABgMbRuAgAAAH7E2bppkp5JkywD7qjoAQAAAIDFkOgBAAAAgMXQugkAAAD4ExOdl+4wyTrgjooeAAAAAFgMiR4AAAAAWAytmwAAAIAfsRmGDJs5eiYdhqGi6l4EykRFDwAAAAAshkQPAAAAACyG1k0AAADAjxgm2nWTA9PNi0QP8LFJD16nu/948VnN+fM/1+qLvUddv4+oW1MPdotR98ubqmVkXdWqEaBjJ/P1zf5jmvfpPm3Ycdgncc81NgCUZ9Qri/Wvhcl687n7FH/HdRWO/fn4Sb3z3qda/dlOpf6Uqbx8uxqE11HHdi00sPcNuukP7Sqcv3HrXr2zaKO27TigrBOnVCekpmIvi9aA269V/z9dI5uNxiYA1kWiB5hQbp7d9b7TZQ01Y0Rn1a9bq9SYJuHBatIxWLd2bKaFm/fryVlbVORweC3u+Y4NwPo+2vid3lm0sUpjP/9mnwY+M13HsnNLfX4484Q+TP5WHyZ/qwG3XaspY+9VQIB7wjb2jQ809d11pT47fuK0Pt32vT7d9r0WrfpK7778kGrXqnHufyAAMDESPcDH/jp3q56dt63CMZ3bNNKMEV1ksxmasnKXdvzvuCSpSXhtzRnZVaHBNZSVm6+JS77V+h2HlW8/o9bRoXr8jlhd1ypSd//xYmXm/KIXF3/rlbiexgaA3/v40x1KGD1TRUWV/1Ao/chxDXjiX8rJ/UXhocF6dvgduumGtqpZI0i7Uw9p4oyP9cU3qVqwcosi69fTPxJ7lZqftOxzV5J3TWwLPTv8DrW5pIkOHc3Wm/PWa/En27T+yz165uVFmjL2Xp/8eQFfMgxDhll6N82yDri5IBO9vXv3avr06dqyZYuysrIUFham9u3bKz4+Xl26dKnu5cFiCgqLVFBY/sbDkaG19ErCtbLZDH22+4gmLPktYUq8rZ1Cg2vol4JC3fXPtdqTfsJ1LTMlT5tSjmj6I511a8dmeujm1pq+Zq+OnsjzOK6nsQGgWFFRkf75zsd6ZeaqKiV5kvTarNXKyf1FtWoGacW0kWp3aZTrWqMG9dS1UysNfGa6Pkz+Vm/NX6/hA25U4wahrjGTZq+WJLW5pImWvz1StWoGSZIi69fTO+MekCQt/mSb5q34UqOH3aamkWHe+cMCgIlccM3p69at05///GctX75cR44ckd1uV2ZmpjZs2KChQ4dq3Lhx1b1EXGBeeeBa1a9bSyd/sWvk9C9UsgPy9qsvkiQt3/q/UolWSf/8wJmg1QgMUNd2TbwS19exAVwY1n2Ros73TtDE6R+rqMihK9o0q9K85eu/kST1vemqUkleMcMw9OzDt0uS7IVntOHL3a5rx0+c0o9pP0uS7r61kyvJK+nBuzpLkhwOh77e9b+z+0MBgJ+4oBK9lJQUPfHEE7Lb7YqNjVVSUpK+/PJLLV68WD169JAkJSUlad68edW8Ulwo7rjmIvW43PlNzEvv/1eHjp92XQsPcT43UlTk0Nf7j5V7jwNHfnt+pVFYbY/j+jo2gAvHXY++pZR9hxQUGKBRD92qmS8+WOmcrF+fyTMMQx3btyh33MXNGrreH8787YdRthKHSNsLz5Q5NygwoMzxgL8o3nXTLC+Y0wXVuvnGG28oLy9PzZs315w5cxQSEiJJCg8P19SpU/XYY49p1apVmjx5snr16qU6depU84phZTUDbRrb70pJUsrB45q7YV+p68dPFajDY0sUFGBTRd+HtGj027/TE6cLPI7ry9gALiyGYej2GztozF/uUEyLxvrpUPk/OCoWEVZHP6yeoAJ7YYWtnvsPZrreh9ULdr0PrRusSy5qqNSfMvX+J9uVeF931axRuqr37vIvJEk1ggJ1VbvmZ/vHAgC/cMFU9FJTU5WcnCxJGjZsmCvJK2YYhkaNGiWbzabs7GytWbOmGlaJC8mgbjGKbuD8d/ji4m/L3bXSfqZI+RU8azfwxstc77f+kFnuuLON64vYAC4sWxeN0dyJQxXTovFZz60RFFhm22WxWUs2u95fd/klpa79/ZFestkM7dl/WH1GTNWmbd8rM+ukdnyfpsfGz9ecDz6TJD314C2lnu0DACu5YCp6mzZtkuRM6OLi4soc06RJE7Vp00a7du3S2rVr1adPn/O5RFxAAmyGHrq5lSRpx/+ytH7HoXO6T8dLGmhQnDPZ+mLvEe0t51k6b8c9l9gALjyXNm/kk/tu/W6/Zix2/v/1G668VG0vbVrq+h3drlDSxKF6bvIH+uKbVN358ORS16MahWvMX+7QPbd28sn6AF9j101UxQVT0du92/mgdtOmTRUREVHuuLZt20qSdu3adV7WhQvTnZ2aq2mEs6o2ZeW5/Vu7pHFdzRzRWYEBNv1SUKgx87afl7jnGhsAvOGHA0d039Pv6MyZItWqGaR/Pt2vzHEnT+UppHbNMq/9fPyktnybqp+Pn/TlUgGgWl0wFb309HRJUnR0dIXjmjZ1/lQwIyNDhYWFCgy8YL5EOI+Kq2qpGTlauf3gWc+/rGk9/efJbmoY6twAZdTcr7Q7LdvncT2JDQCe2rP/sPqOmKrMLGeC9tqou9X+MvddOf/6yiL9e6HzYPYH/9xZw+7pqhZRDXQsO1cr1v9X497+ULOXfKYvvknV8rcfVWT9euf1zwEA58MFU9E7ftx5EHRoaMW9+HXr1pXk3HI5JyfH5+vChefiRnV1eYv6kqRFn//odqxBZTpd1lAfjLpJTcKdmw+Mnb9diz7/0edxPYkNAJ764r+puvWhSa4dNl964s8acPt1buOSt+xxJXl/H9FLr466WzEtGqtGUKCaNAzTQ3ffqJX/fkwhtWto748ZeuGtFef1zwF4Q3HrplleMKcLJtHLz8+XJNWsWXYbR7FatWq53hcUsIsgvO/Wjr+dI7Vsy9md39Tnuhb6z1PdFFGnps4UFemp2Vs0Y+1en8f1NDYAeOK9j79Sn0em6PiJ07LZDL3+twEaPqDs5+3nLv1ckvM5vMT7upc5JjYmWgl9/yhJ+s/KLTqdx/+/B2A9F0yiFxAQUPkg4DwoTri+2X9M/8vMrWT0b0be3k5vPnSDagUF6HR+oQZP3aT5n6b6PK43YgPAuXplxioNe26O8gsKFVyrhpImDtWgPn8od/y+n45Kkq6JbaGAgPK/zflDR+dmUoVnivRjGrsGA7CeC+YBtNq1nc8TVValy8vLc72vrPoHnK2IOjXVoblzM6CPv676M3Iv3ne1HugWI0k6euIXPTD5U/33x8rPo/I0rjdiA8C5euqfC127a0ZG1NX814apY7sWFc6x2wslSfkFhVWOU3AWYwEzMNVB5WZZB9xcMBW94mfvTp6seIet4ufyAgICKn2eDzhb11zaQLZfTyD/Zn/VkqUx/a5wJVqpGTm6Y/zqs060ziWut2IDwLl4bvJSV5J3yUUNtXrmk5UmedJvRzp8+W2q8gvs5Y774r/OroTAAJsubtbQ8wUDgMlcMIley5YtJUmHDlV8btjhw4clSY0aNZLNdsF8eXCedGjhrKoVFTn07YHKE6aeV0XrL39yHvmRmpGjPhPW6uDPp3we15uxAeBsfbjhW01JWitJuvSiSH3078fVPKpBleb2vbmjJOn4idMaV85GK3v2H9bMX5PIm/7QTqF1g72wagAwlwumdTMmxlmVOHjwoHJzc1WnTp0yx6WkpEiS2rRpc97WhgvHZU2dVeKfT+YpN6/iVqEagTaNi79akpRnP6NHp3+h0/mFCq5Z/v9s7YVFsp8p8iiut2MDwNnIL7Drr68skiTVrBGot/9voIJr11Tu6fxy59QIClCNIOd/n3r3uFKzl8To023fa+q89frfoWMadk+cWl/cWKdO52vVph166d8fKfd0vurVqa3nH+19Pv5YgHeZaLdLh0nWAXcXTKLXtWtXvfDCCzpz5oySk5N1++23u405fPiw62D1zp07n+8l4gIQXd95WHnO6cp3eLv96ovUNML5U+ZaQQFaOeaWSue8umyHXl22w6O43o4NAGdj6dpvdOhotiTnc3Y3JbxS6Zy/Dv2TRj10myTntvNzJw7RA6NmKHnrXq3Y8K1WbPjWbU5kRF3NmTjE1eoJAFZzwfQmNmvWTB07Ots5pkyZ4vasnsPh0IQJE1RUVKTw8HD16tWrOpYJi6sXXEOSdOJ0+c+NFOt4SdXalLwd19uxAeBsbNt5wON7hNYN1vtTHtHsCYPVs3N7NapfT0GBAapXp7auattczw6/XVsWjdV1l1/i+YIBwKQMh+Ncjk32Tzt27FD//v1VVFSkmJgY/fWvf1Xbtm11+PBhvf3221qzZo0k6bnnntO9997r1dgZGRnq2rWrJOnYFY+rqGY9r94fAKrLoZnx1b0EAPCKIxkZurm78/u1jRs3qnHjxtW8ot+U/F6ydp8XZQsOr+YVORWdPq5fPvibJPN9zS50F0zrpiTFxsZq/PjxGjt2rL7//nsNHjzYbUxCQoLXkzwAAAAA5y43N1dz587V2rVr9dNPPyk/P19NmzZV165dNXjwYDVqVH4b9unTpzVr1iytWrVKP/30kwICAtS8eXP96U9/0sCBA1WrVq0KY2/btk2zZ8/W119/rZycHEVERKhjx44aOHCgrrzyygrnehrbExdUoidJffv2Vbt27TRjxgxt2bJFx44dU3BwsNq3b6/4+Hj16NGjupcIAAAA4Fd79uzR0KFDdfTo0VKfHzhwQAcOHNCyZcv0zjvvqEOHDm5zjx8/rnvvvVepqamlPk9JSVFKSoo++OADzZ49u9xEcd68eXrhhRdUsgnyyJEj+uijj7Rq1So99dRTZRaPvBHbUxdcoidJrVq10sSJE6t7GQAAAMBZM2SeXTcNH5+YnpmZqUGDBik7O1t169bV448/rhtvvFGFhYXasGGDXn/9dWVnZ+uRRx7Rxx9/XGpn/aKiIj388MNKTU1VSEiInnrqKXXv3l1nzpzRRx99pMmTJ2v//v0aMWKEFi5c6Ha02saNGzVu3Dg5HA517txZjz76qJo1a6bU1FRNmjRJ27Zt08svv6yLL75YcXFxpeZ6GtsbLpjNWAAAAAD4lwkTJig7O1vBwcGaNWuW7r33XkVFRal58+Z64IEH9Prrr0uSjh49qmXLlpWau3r1an3zzTeSpNdff13x8fFq1KiRmjZtqiFDhmjy5MmSpO+++04rV64sNdfhcOiVV15RUVGRrrrqKk2bNk0dOnRQeHi4rr76as2aNUtXXnmlHA6H/vnPf6qoqMhrsb2FRA8AAACA6fz888/6+OOPJUkPP/ywYmNj3cbceOONatGihYKCgrRr165S12bNmiVJuuaaa9SlS5cy595www2SpPfee6/Utc2bN+v777+XJI0cOVKBgaUbIWvUqKGnn35akvTjjz9q27ZtXovtLSR6AAAAgB8xDHO9fOWTTz7RmTNnVLt2bd13333ljlu+fLl27typF1980fVZdna2vv3WeYZm9+7dy51bfG3btm06ceKE6/ONGzdKkurVq6drrrmmzLlXXXWVwsOdu5+uXbvWa7G9hUQPAAAAgOl89913kpw75wcHB5e6Zrf/djZwzZo13ebu2bPHtYFKu3btyo3Rpk0bSc5n6lJSUkrNl6TWrVsrICCgzLmGYbjml6wmehrbWy7IzVgAAAAAeNfvd8Usy9mcs/fDDz9Iklq0aCFJWrdund59913997//1enTp9WwYUP16NFDDz/8sNvOlenp6a730dHR5caIiopyvU9LS3ObX9FcSWratGm5c881treQ6AEAAAB+xDBMtOtmiXX069ev0vF79+6t8r2LE8fQ0FA999xzWrhwYanrmZmZWrBggT7++GNNmzat1Jl2x48fd72vV69euTFK7tKZk5PjNj80NLTCNdatW7fcueca21to3QQAAABgOqdOnZIkLV26VAsXLtTVV1+tefPm6bvvvtMXX3yh5557TsHBwcrOztZf/vKXUhXF/Px81/uKDiUvea3knOL3ZbWFllR8vay55xrbW6joAQAAAPDYokWLFBkZ6bX75eXlSXJW7q699lrNmDFDQUFBkpwJ1r333quYmBgNHDhQWVlZ+ve//60xY8ZIUrnP1VVVQECA25EJZzPXDKjoAQAAAH6kunfZLG/XzcjISDVu3LjC19koWfEaNWqUK8kr6ZprrlHXrl0lOc+uK1a7dm3X+4qqZcXJ5O/jFc8vKCiocI1lVf48je0tJHoAAAAATCckJESS8zm4tm3bljuuU6dOkqQjR44oNzdXUuln44o/K8vJkydd74uPSiiO+fvrZSl+tq7kXE9jewuJHgAAAADTKd6xsrLn5EpualJcJSveqVMqvQvm7x06dMj1vkmTJq73LVu2dLtelsOHD0v6bfdNb8T2FhI9AAAAwI8U77pplpevFJ8zl5WVVWFl7Oeff5YkBQUFKSIiQpJ02WWXuda2e/fucucWn19nGIZat27t+jwmJkZS6TPxfs/hcLjuXbxWb8T2FhI9AAAAAKZz4403SnIeKL527dpyx3322WeSpA4dOshmc6Y3derUUceOHSVJ69evL3du8bUOHTooLCzM9XmXLl0kSceOHdN///vfMud+/fXXrqMUOnfu7Prc09jeQqIHAAAAwHT+8Ic/uA4VnzRpkqtyV9KqVau0bds2SVKfPn1KXevdu7ckafPmzUpOTnabm5ycrM8//1yS9MADD5S6du2117piT5w40W1TloKCAr3yyiuSnBW8komep7G9hUQPAAAA8CPVvctmebtueltgYKCef/552Ww2ZWRkqH///lq2bJmOHDmi9PR0TZs2TU899ZQk6YorrlDfvn1Lze/bt69rE5eRI0dq5syZysjIUEZGhmbOnKmRI0dKki6//HL17Nmz1FybzabRo0dLclbuHnzwQW3fvl3Hjx/X9u3b9eCDD+rrr7+WYRh64okn3FpYPYntLYajvKZTeFVGRoZr69djVzyuopr1KpkBAP7h0Mz46l4CAHjFkYwM3dzd+f3axo0bz/o4AF8q+b1kaP9/yhbi/V0az0XRqeM68d5fJfnua7Zy5Ur97W9/K3UcQUnt2rXTW2+9VWbs9PR0DRo0SAcPHixzbsuWLTV//nzXs32/9+abb2ry5MllXjMMQ3/72980cODAMq97GttTHJgOAAAAwLRuu+02XXnllZo1a5Y+/fRTZWRkqGbNmmrZsqXuvPNO/fnPfy73HLqoqCgtW7ZMs2fP1ieffKKDBw/qzJkzat68uW655RYlJCS4jnEoyyOPPKJrr71Wc+fO1fbt25Wdna169erpyiuv1AMPPOA62sEXsT1FRe88oaIHwKqo6AGwCn+p6IXd/U/ZQnxTBTpbRaeylL3QtxU9nBue0QMAAAAAiyHRAwAAAACL4Rk9AAAAwJ/4eLfLs2KWdcANFT0AAAAAsBgSPQAAAACwGFo3AQAAAD9iGIbbAd3VxSzrgDsqegAAAABgMSR6AAAAAGAxtG4CAAAAfsSQeXbdNMkyUAYqegAAAABgMSR6AAAAAGAxtG4CAAAAfoRdN1EVVPQAAAAAwGJI9AAAAADAYmjdBAAAAPwIrZuoCip6AAAAAGAxJHoAAAAAYDG0bgIAAAB+xDBMdGC6SdYBd1T0AAAAAMBiSPQAAAAAwGJo3QQAAAD8CLtuoiqo6AEAAACAxZDoAQAAAIDF0LoJAAAA+BF23URVUNEDAAAAAIsh0QMAAAAAi6F1EwAAAPAj7LqJqqCiBwAAAAAWQ6IHAAAAABZD6yYAAADgT0y066bMsg64oaIHAAAAABZDogcAAAAAFkPrJgAAAOBHbIYhm0l6N82yDrijogcAAAAAFkOiBwAAAAAWQ+smAAAA4EcMmWfXTZMsA2WgogcAAAAAFkOiBwAAAAAWQ+smAAAA4EcMw5Bhkt5Ns6wD7qjoAQAAAIDFkOgBAAAAgMXQugkAAAD4EZvhfJmBWdYBd1T0AAAAAMBiSPQAAAAAwGJo3QQAAAD8CLtuoiqo6AEAAACAxZDoAQAAAIDF0LoJAAAA+BHDcL7MwCzrgDsqegAAAABgMSR6AAAAAGAxtG4CAAAAfsYQPZOoGBU9AAAAALAYEj0AAAAAsBhaNwEAAAA/YjOcLzMwyzrgjooeAAAAAFgMiR4AAAAAWAytmwAAAIAfMQxDhklOKjfLOuCOih4AAAAAWAyJHgAAAABYDK2bAAAAgB8xDOfLDMyyDrijogcAAAAAFkOiBwAAAAAWQ+smAAAA4EdshiGbSXomzbIOuKOiBwAAAAAWQ6IHAAAAABZD6yYAAADgRwyZZ7dLkywDZaCiBwAAAAAW49WK3ujRo712L8Mw9OKLL3rtfgAAAABwofBqovfBBx/I8EId2eFwkOgBAAAAZTAMwyvfc3vD+VjHuHHjlJSUVOm4sWPH6r777iv1md1u14IFC7R8+XKlpqbK4XAoKipKPXr0UEJCgsLCwiq85969ezV9+nRt2bJFWVlZCgsLU/v27RUfH68uXbpUONfT2J7yaqLXtGlTb94OAAAAwAVu165d5zQvPz9fQ4YM0datW0t9vm/fPu3bt09LlizRjBkzFBMTU+b8devWaeTIkbLb7a7PMjMztWHDBm3YsEH333+/xowZ45PY3uDVRG/9+vXevB0AAACAC1hRUZH27NkjSfr73/+uXr16lTu2Ro0apX4/evRobd26VUFBQRoxYoRuv/121ahRQxs3btTLL7+so0ePavjw4frwww8VHBxcam5KSoqeeOIJ2e12xcbG6plnntFll12mtLQ0TZs2TWvXrlVSUpJatmype++9120tnsT2FjZjAQAAAPyJ4dx10wwvX2+7+eOPP+r06dOSpI4dOyokJKTcV1BQkGvejh07tHLlSknSs88+q+HDhys6OlqRkZHq16+fZs+eraCgIKWnp2vu3Llucd944w3l5eWpefPmmjNnjjp16qTw8HDFxsZq6tSp6tmzpyRp8uTJys3NLTXX09jeUi2J3okTJ7Rnzx5t27bN9VnxXyAAAAAASL+1bQYHB+vSSy+t8rxZs2ZJkqKjo9W/f3+3623btlXv3r0lSYsWLSp1LTU1VcnJyZKkYcOGKSQkpNR1wzA0atQo2Ww2ZWdna82aNV6L7U3nLdErKCjQ7Nmzddttt+m6665Tnz59NHDgQNf1QYMGaejQofrhhx/O15IAAAAAmFhKSookZ3IUEBBQpTkOh0ObNm2SJMXFxZU7r3v37pKktLQ0V3uoJNdcwzAUFxdX5twmTZqoTZs2kqS1a9d6LbY3nZcD04t7UHfv3i2Hw1HmmJ9++kk7d+7UV199pcmTJ1e6iw0AAABwIbIZhmwm2XXT1+sorui1adNG7733npYvX67du3fLbrcrKipK3bt31+DBgxUeHu6ak5aWppycHElSu3btyr1327ZtXe937typ1q1bS5J2794tybnRZERERIXzd+3aVWqzGE9je5PPE73CwkINHz5cKSkpCggI0K233qrLL79c48aNKzXu5ptv1tKlS5WXl6cnnnhCK1euVKNGjXy9PAAAAABecPTo0UrHNG7cuMr3czgcroref/7zn1K7X0rS/v37tX//fr3//vt6++23dcUVV0iS0tPTXWOio6PLvX/Dhg0VFBQku92utLQ01+fF8yuaK/124kBGRoYKCwsVGBjocWxv8nmit3jxYqWkpKhu3bqaOXOmYmNjdfr0abdE74UXXtBdd92lhx56SDk5OUpKStJTTz3l6+UBAAAA8IJ+/fpVOmbv3r1Vvt///vc/10YnhYWFuvvuu9W/f39FRUUpMzNTK1as0MyZM5WVlaWHHnpI77//vpo1a6bjx4+77lGvXr1y72+z2RQSEqLs7GxXFU6Sa35oaGiF66tbt64kZ0Kak5OjiIgIj2N7k8+f0Vu5cqUMw9Ajjzyi2NjYCsdefvnlGjFihBwOhzZu3OjrpQEAAAB+xzDZy1eOHDmixo0by2azacKECXr++efVvn17hYeHKyYmRk8++aQmTZokybnZ48svvyzJeYZdsVq1alUYo2bNmm5zit8XXytPyXsXFBR4JbY3+byi9/3330uSevToUaXxN954o8aPH++zEiYAAAAA71u0aJEiIyO9dr9rr71WGzduVEFBgdsZecVuvvlmxcXFacOGDVqzZo1OnDhR5U1byuPJfE9je5PPE71ffvlFUsWly5Lq1KkjyXk4IgAAAAD/EBkZeVbP4FVVeUlese7du2vDhg0qKirSzp07Vbt2bde1yqplxddLVt+K5xdX6cqTl5fnel9cnfM0tjf5vHWzfv36kpznUVRF8QOXDRo08NmaAAAAAH9lGIapXtWtSZMmrvdZWVmlCkwnT54sd15RUZFOnTolSaV27Sx+9q6iuZJcz9YFBAS4nufzNLY3+TzR69ixoyRp9uzZlY4tKirS22+/LcMwdNVVV/l4ZQAAAADMrrzj2YqV3I2zdu3aatGihev3hw4dKndeZmama27JZLFly5aVzpWkw4cPS5IaNWokm82ZVnka25t8nujdd999cjgcWr16tV588cVSJc6Sjhw5osTERG3btk2SdPfdd/t6aQAAAABM6sknn9S1115b6V4f+/btc71v2bKlIiMjFRYWJum3bsGylDz/ruS5djExMZKkgwcPunb9LEvxvYsPTpfkcWxv8nmid8UVV+iBBx6Qw+FQUlKSrr/+eg0cONB1/cknn9Q999yj7t27a/369ZKku+66S1dffbWvlwYAAAD4HZthrpev1KtXT9nZ2UpLSyuVzJXkcDi0cuVKSVJUVJQuvvhiSVLXrl0lScnJyeVWBItzj4YNG5Y6sLx47pkzZ5ScnFzm3MOHD7sOVu/cuXOpa57E9iafJ3qS9Ne//lXDhw+XYRj65ZdftHPnTlc/70cffaRvv/1WhYWFcjgcio+P19///vfzsSwAAAAAJnXHHXe43o8fP77MpOmdd95xJVyDBw925Rh9+vSR5DxUff78+W7zUlJStHTpUknSoEGDSj1r2KxZM9fjZ1OmTHF71s7hcGjChAkqKipSeHi4evXqVeq6J7G96bwkeoZh6LHHHtPHH3+sBx98UO3bt1e9evUUEBCgkJAQXXrppYqPj9fSpUv13HPPKTDQ55uBAgAAADCxq666Srfffrsk6fPPP9egQYO0detWZWVlac+ePRo7dqxeffVVSVKnTp00YMAA19zrr79e3bp1k+RMEidNmqSDBw8qMzNTixcvVkJCgux2u6Kjo0vNKzZ69GjZbDYdOHBA8fHx2rx5s7KysrRr1y4lJiZq1apVkqTExEQFBweXmutpbG8xHJU93QivyMjIcJVxj13xuIpqVu24CQAwu0Mz46t7CQDgFUcyMnRzd+f3axs3bvTJUQHnquT3kpePfFs16tWv5hU5FeQc07dvPCzJN1+zvLw8PfbYY9qwYUO5Y2644QZNmTLFdUxbsRMnTmjw4MHasWNHmfMaNGig+fPnq3nz5mVeX7JkicaOHavCwsIyryckJGjUqFFlXvM0tjdQOgMAAABgSrVq1dLbb7+t1atX6/3339eOHTt08uRJhYaGqnXr1urTp49uu+22MtsfQ0NDtWDBAi1YsEArVqxQamqqCgoKFBUVpbi4OA0dOtR1FFxZ+vbtq3bt2mnGjBnasmWLjh07puDgYLVv317x8fEVbhLjaWxvOK+JXmZmpj788EN99dVXOnTokPLy8lSvXj1ddNFFuvrqq3X77be7ZeIAAAAALlyGYeiWW27RLbfcctZzg4KCNHDgwFKbQZ6NVq1aaeLEiec019PYnjoviV5RUZFee+01zZ49W2fOnJFU+jyMHTt2aOXKlXr55Zc1atQo9evX73wsCwAAAPA7huF8mYFZ1gF35yXRe/LJJ7Vq1So5HA4ZhqFLL71ULVq0UO3atXXq1Cnt379fP/74o06dOqXnnntOR48e1SOPPHI+lgYAAAAAluPzRG/t2rX6+OOPZRiGbr75Zo0aNUpNmzZ1G/fDDz/o+eef11dffaWpU6fqD3/4g6644gpfLw8AAAAALMfnxyv85z//kSR1795dkydPLjPJk6TLLrtMM2fOVMeOHeVwODRz5kxfLw0AAADwP4YhwyQvejfNy+eJXvHh6A8//HClY4OCgvT4449LkrZv3+7rpQEAAACAJfk80cvLy5MkRUdHV2n8pZdeKknKzc312ZoAAAAAwMp8nuhdfPHFkpzP4FXFwYMHJUnNmjXz2ZoAAAAAf2WTZDNM8qruLwbK5fO/mwEDBsjhcOjVV19VQUFBpePfeustSc4DCgEAAAAAZ8/niV6/fv1022236ZtvvtEDDzyg3bt3lzkuIyNDjz76qJKTk9WlSxc98MADvl4aAAAAAFiSV49X6N69e5mfFx+O/s0336hv375q2rSpWrZsqeDgYOXl5Sk9PV0//vijHA6HAgICJEmPPPKI3n77bW8uDwAAAPB7rh0vTcAs64A7ryZ66enpFV4vTvjS09PLHVtYWKhPP/2UfzQAAAAAcI68muj16dPHm7cDAAAAAJwDryZ6L730kjdvBwAAAOB3jF9fZmCWdcAdO6ICAAAAgMWYNtHLzs6u7iUAAAAAgF/yautmRdLT0/X5558rKytLhYWFro1ZijkcDtntdp06dUoHDhzQ119/rW+++eZ8LQ8AAADwCzbDkM0kGxeaZR1wd14SvalTp+rtt99WUVFRlcY7HA523QQAAACAc+TzRO+zzz7T1KlTqzzeMAzFxsaqW7duPlwVAAAAAFiXz5/RW7RokSQpPDxcb775prZt26Y333xTkjRgwADt2rVLmzdv1sSJE9WoUSNJUsOGDfXwww/7emkAAACA3zEMc71gTj5P9L777jsZhqGHH35Y3bt3V506dXTNNdfIMAx99tlnCggIUIMGDXTnnXfqP//5j0JDQ7VhwwZt2LDB10sDAAAAAEvyeaJ3/PhxSdIf/vAH12f16tVTkyZNdPDgQdd1SWrSpImGDBkih8Oh999/39dLAwAAAABL8nmiV7wBS4MGDUp9fvHFF0uSfvjhh1Kfx8XFSZJSUlJ8vTQAAADA7zhbJg2TvKr7q4Hy+DzRCwsLk6RSlTtJatasmST3RK9+/fqSpGPHjvl6aQAAAABgST5P9Nq2bStJ2rRpU6nPmzdvLofDoZ07d5b6PCMjQ5LcztkDAAAAAFSNzxO9bt26yeFwaNKkSVq5cqWrlbNDhw6SpDVr1ig9Pd01/p133pHkfF4PAAAAwO+YYKdN146btG6als8Tvd69e6t58+Y6ffq0nnrqKT399NOSpCuvvFIXXXSRTp06pT59+uixxx7TnXfeqY8++kiGYahLly6+XhoAAAAAWJLPE72goCBNnz5drVq1ksPhcG3KYhiGnn/+eQUEBCgnJ0effPKJfvjhB9eY4cOH+3ppAAAAAGBJgecjSLNmzfTBBx9o06ZNCgkJcX1+3XXXaf78+Xr99df1zTffKDAwUH/84x/11FNPuTZlAQAAAPAbm2HIZpLtLs2yDrg7L4meJNlsNnXt2tXt8w4dOmjmzJnnaxkAAAAAYHk+b90EAAAAAJxf562iBwAAAMBzrh0vTcAs64A7ryZ6bdq08dq9DMNQSkqK1+4HAAAAABcKryZ6HHIOAAAAANXPq4neiBEjvHk7AAAAAL9jGIYMk/RMmmUdcEeiBwAAAAAWw2Ys1WDLxDvVqHHj6l4GAHhF+DX8kA+ANQQU5SmquhcBeAmJHgAAAOBHbDLPGWlmWQfc8XcDAAAAABZDogcAAAAAFkPrJgAAAOBHDJlo102ZYx1wR0UPAAAAACyGRA8AAAAALIbWTQAAAMCPGIZkM0nHpEk6SFEGKnoAAAAAYDHntaJ37NgxffDBB9q+fbsyMjJ06tQprV69WpL02muvqXXr1rr11lvP55IAAAAAwHLOW6L3zjvvaMqUKbLb7ZIkh8NRaregDz/8UO+8844WLFigyZMnKzw8/HwtDQAAAPAbNhO1bpplHXB3Xlo3X331Vb322msqKChQzZo11aFDB7cxeXl5cjgc2rZtmx5++GE5HI7zsTQAAAAAsByfJ3rffvut3nnnHUnSfffdp88++0wzZ850G7d27Vrdd999cjgc+vbbb7VkyRJfLw0AAAAALMnniV5SUpIk6dZbb9WYMWMUEhJS5gGPwcHBGjNmjHr16iWHw6EVK1b4emkAAACA3zEMw1QvmJPPE71t27bJMAwNGjSoSuPvv/9+SdKePXt8uSwAAAAAsCyfJ3rHjh2TJLVo0aJK46OjoyVJubm5vloSAAAAAFiaz3fdDAkJ0YkTJ5Sdna169epVOj4zM1OSVLduXV8vDQAAAPA77LqJqvB5Re+yyy6TJK1Zs6ZK4z/44ANJUkxMjM/WBAAAAABW5vNE79Zbb5XD4dCbb76pHTt2VDj2k08+0Zw5c2QYhm6++WZfLw0AAAAALMnnrZv9+vXT/Pnz9cMPPyg+Pl633367Wrdu7br+2WefKS0tTevWrdOmTZvkcDjUokUL9evXz9dLAwAAAPyOYThfZmCWdcCdzxO9wMBAvfPOO0pISNCPP/6opUuXSpJrK9YhQ4a4xjocDjVp0kTTpk1TjRo1fL00AAAAALAkn7duSlLjxo21ZMkSPfzww4qIiJDD4XB7hYSEaODAgfrggw+qvEMnAAAAAMCdzyt6xWrXrq2RI0dq5MiR2rdvnw4ePKjc3FzVrl1bTZo0UevWrRUQEHC+lgMAAAD4JZthyGaSnkmzrAPuzluiV9Kll16qSy+9tDpCAwAAAIDlnZfWTQAAAADA+ePzit7o0aPPaZ5hGHrxxRe9vBoAAAAAsD6fJ3offPCBa4fNqnI4HCR6AAAAQBkMmactjyf0zMvniV7Tpk0rvJ6fn6+cnBzZ7XZJUmhoqG666SZfLwsAAAAALMvnid769esrHVNUVKSdO3dq6tSp2rRpk2rXrq1nn33W10sDAAAAAEsyRdXXZrOpQ4cOmjZtmq655hq9++67Sk5Oru5lAQAAAKZjGOZ6wZxMkegVs9lsevjhh+VwOPTuu+9W93IAAAAAwC+ZKtGTpFatWkmSdu7cWc0rAQAAAAD/VC0HplckMzNTkpSXl1fNKwEAAADMx2YYspmkZ9Is64A70yV6b731liQpOjq6mlcCAAAAwIxOnz6tPn366MCBAxoxYoQSExPLHGe327VgwQItX75cqampcjgcioqKUo8ePZSQkKCwsLAK4+zdu1fTp0/Xli1blJWVpbCwMLVv317x8fHq0qVLhXM9je0pnyd6S5curXSM3W5XVlaWPvnkE+3evVuGYXDEAgAAAIAyTZgwQQcOHKhwTH5+voYMGaKtW7eW+nzfvn3at2+flixZohkzZigmJqbM+evWrdPIkSNdx8BJzu7DDRs2aMOGDbr//vs1ZswYn8T2Bp8neqNGjTqrA9MdDoeaNWumwYMH+3BVAAAAgH8yZJ7dLqtjGcnJyVq4cGGl40aPHq2tW7cqKChII0aM0O23364aNWpo48aNevnll3X06FENHz5cH374oYKDg0vNTUlJ0RNPPCG73a7Y2Fg988wzuuyyy5SWlqZp06Zp7dq1SkpKUsuWLXXvvfd6Nba3nJfNWBwOR5Ve9erVU79+/TRv3jzVqVPnfCwNAAAAgJ/Iysqq0nnbO3bs0MqVKyVJzz77rIYPH67o6GhFRkaqX79+mj17toKCgpSenq65c+e6zX/jjTeUl5en5s2ba86cOerUqZPCw8MVGxurqVOnqmfPnpKkyZMnKzc316uxvcXnFb1169ZVOiYgIEC1a9dWaGior5cDAAAAwE+NGTNGP//8s/r27aslS5aUO27WrFmSnPt+9O/f3+1627Zt1bt3by1atEiLFi3S8OHDXddSU1NdZ3oPGzZMISEhpeYahqFRo0Zp9erVys7O1po1a9SnTx+vxPYmn1f0Dh48qMDAQEVFRZX7aty4MUkeAAAAUAU2w1yv82XRokVat26doqKiKqzqORwObdq0SZIUFxengICAMsd1795dkpSWlqY9e/a4Pi+eaxiG4uLiypzbpEkTtWnTRpK0du1ar8X2Jp8neuPHj1e3bt2qtCkLAAAAAPzeTz/9pBdffFGGYeill16q8DGvtLQ05eTkSJLatWtX7ri2bdu63pc8w3v37t2SpKZNmyoiIqLS+bt27fJabG/yeetmWlqaioqKdOWVV/o6FAAAAIBqcvTo0UrHNG7c+Kzve+bMGT3zzDM6ffq0Bg0apGuvvbbC8enp6a73FR3Z1rBhQwUFBclutystLc1tfmXHvTVt2lSSlJGRocLCQgUGBnoc25t8nugFBQUpLy9PNWvW9HUoAAAAwPLMemB6v379Kh2/d+/es47xr3/9S998840uueQSPfnkk5WOP378uOt9vXr1yh1ns9kUEhKi7OxsVxWu5PzKHi2rW7euJGe7Zk5OjiIiIjyO7U0+b93s2bOnHA6Hpk+f7utQAAAAACxk586deuuttxQYGKiJEydWqXiUn5/vel+rVq0Kxxbfr+Sc4veVxSp574KCAq/E9iafV/T+9re/KTMzU/PmzdOePXvUs2dPtWnTRhEREZV+8YrLoQAAAADMbdGiRYqMjPTa/fLy8vT000/LbrcrMTFR7du3r9K88jZAqSpP5nsa25t8nugVV/QcDoe2b9+u7du3V2meYRhKSUnx8eoAAAAAP2OY58D0kiemR0ZGntMzeOWZOHGi9u/fr9jY2LM6gqB27dqu95VVy4qvl6y+Fc8vrtKVJy8vz/W+uIDlaWxv8nmil5GR4XrvcDh8HQ4AAACAn9u0aZPmzZunmjVr6p///KcCA6uetpR8Nu7kyZPljisqKtKpU6ckSeHh4a7Pi5+9q2iuJNezdQEBAa7n+TyN7U0+T/ReeuklX4cAAAAAYCErV66U5Kx63XrrrRWOnTp1qqZOnSpJWrdunVq0aOG6dujQIXXs2LHMeZmZmbLb7ZKc5+IVa9mypbZu3apDhw5VGPfw4cOSpEaNGslmc2594mlsb/Jqold8Vt6dd97p+sOWPCUeAAAAgGfO90HlFTHLOkqKjIxUWFiYsrOzlZKSojvuuKPMcSXPvyt5rl1MTIwk6eDBg8rNzS33zL7ix8yKD073Rmxv8uqum6NGjdLf/vY3n+0cAwAAAMD6nn/+eX399dcVvooNGzbM9VlUVJQkqWvXrpKk5OTkch8fW79+vSTnmXatW7d2fV4898yZM0pOTi5z7uHDh10Hq3fu3LnUNU9ie5PXj1fgOTwAAAAAnqhRo4ZCQkIqfBULCgpyfWb8uktNcVfh/v37NX/+fLf7p6SkuLoRBw0a5JonSc2aNXO1XE6ZMsXtWTuHw6EJEyaoqKhI4eHh6tWrV6nrnsT2Jp+fowcAAADAewyT/TKj66+/Xt26dZMkjR8/XpMmTdLBgweVmZmpxYsXKyEhQXa7XdHR0RowYIDb/NGjR8tms+nAgQOKj4/X5s2blZWVpV27dikxMVGrVq2SJCUmJio4ONirsb3F55uxAAAAAMD5NmHCBA0ePFg7duzQtGnTNG3atFLXGzRooJkzZ5b5DF5sbKzGjx+vsWPH6vvvv9fgwYPdxiQkJOjee+/1emxvIdEDAAAAYDmhoaFasGCBFixYoBUrVig1NVUFBQWKiopSXFychg4dqvr165c7v2/fvmrXrp1mzJihLVu26NixYwoODlb79u0VHx+vHj16+Cy2N/gk0fNVnykAAABwoWPXTae9e/dWOiYoKEgDBw7UwIEDzylGq1atNHHixHOa62lsT/kk0RsyZIjreIVzZRiG5syZ46UVAQAAAMCFwyeJ3vbt2z2a73A4qAoCAAAAwDnySaLnq9PdAQAAgAsdrZuoCp8keitXrlTt2rV9cWsAAAAAQCU4Rw8AAAAALIbjFQAAAAA/Yhjm2eXeJMtAGajoAQAAAIDFkOgBAAAAgMXQugkAAAD4EcNEu27SumleXk30XnrpJUlSzZo1vXlbAAAAAMBZ8Gqi16dPH2/eDgAAAABwDmjdBAAAAPyIIfO0TJpkGSgDm7EAAAAAgMWQ6AEAAACAxdC6CQAAAPgRm2HIZpLeTbOsA+6o6AEAAACAxZDoAQAAAIDF0LoJAAAA+BGbiQ5MN8s64I6KHgAAAABYDIkeAAAAAFgMrZsAAACAHzEMEx2YbpJ1wB0VPQAAAACwGBI9AAAAALAYWjcBAAAAP2LIkE3m6Jk0TLIOuKOiBwAAAAAWQ6IHAAAAABZD6yYAAADgR9h1E1VBRQ8AAAAALIZEDwAAAAAshtZNAAAAwI/YDOfLDMyyDrijogcAAAAAFkOiBwAAAAAWQ+smAAAA4EdshiGbSba7NMs64I6KHgAAAABYDIkeAAAAAFgMrZsAAACAH+HAdFQFFT0AAAAAsBgSPQAAAACwGFo3AQAAAD/CrpuoCip6AAAAAGAxJHoAAAAAYDG0bgIAAAB+ho5JVIaKHgAAAABYDIkeAAAAAFgMrZsAAACAH7HJPNUas6wD7vi7AQAAAACLIdEDAAAAAIuhdRMAAADwI4ZhyDDJtptmWQfcUdEDAAAAAIsh0QMAAAAAi6F1EwAAAPAjxq8vMzDLOuCOih4AAAAAWAyJHgAAAABYDK2bAAAAgB+xGYZsJtnt0izrgDsqegAAAABgMSR6AAAAAGAxtG4CAAAAfoRdN1EVVPQAAAAAwGJI9AAAAADAYmjdBAAAAPyIYThfZmCWdcAdFT0AAAAAsBgSPQAAAACwGFo3AQAAAH9iGDLM0jNplnXADRU9AAAAALAYEj0AAAAAsBhaNwEAAAA/YpN5qjVmWQfc8XcDAAAAABZDogcAAAAAFkPrJgAAAOBHDBPtunm+1rF69Wq999572rFjh06dOqUGDRroyiuvVP/+/XX99deXO89ut2vBggVavny5UlNT5XA4FBUVpR49eighIUFhYWEVxt27d6+mT5+uLVu2KCsrS2FhYWrfvr3i4+PVpUuXCud6GttTJHoAAAAATMlut+vpp5/Wxx9/XOrzw4cP6/Dhw/roo49099136//+7//cks78/HwNGTJEW7duLfX5vn37tG/fPi1ZskQzZsxQTExMmbHXrVunkSNHym63uz7LzMzUhg0btGHDBt1///0aM2ZMmXM9je0NtG4CAAAAMKVXX33VleT17NlT7733nj7//HMtWrRIPXv2lCQtXLhQ06ZNc5s7evRobd26VUFBQXr88ce1bt06bdq0SePGjVNoaKiOHj2q4cOH6/Tp025zU1JS9MQTT8hutys2NlZJSUn68ssvtXjxYvXo0UOSlJSUpHnz5pW5bk9iewuJHgAAAOBHDJO9fOXIkSN69913JUm33Xab3njjDV1++eWqX7++OnTooDfeeEPdunWTJM2cOVP5+fmuuTt27NDKlSslSc8++6yGDx+u6OhoRUZGql+/fpo9e7aCgoKUnp6uuXPnusV+4403lJeXp+bNm2vOnDnq1KmTwsPDFRsbq6lTp7qSzMmTJys3N7fUXE9jewuJHgAAAADT2bBhg6tt8pFHHilzzJ133ilJysnJ0Y8//uj6fNasWZKk6Oho9e/f321e27Zt1bt3b0nSokWLSl1LTU1VcnKyJGnYsGEKCQkpdd0wDI0aNUo2m03Z2dlas2ZNqeuexPYmEj0AAAAApnPPPfdo48aNmj17ti655JJKxwcGOrcfcTgc2rRpkyQpLi5OAQEBZY7v3r27JCktLU179uxxfV481zAMxcXFlTm3SZMmatOmjSRp7dq1rs89je1NJHoAAACAHzGM33berP6Xb/+sjRs3LndXTbvdrvnz50uSoqKi1KJFC0nO5CknJ0eS1K5du3Lv3bZtW9f7nTt3ut7v3r1bktS0aVNFRERUOn/Xrl2uzzyN7U3sugkAAADAY0ePHq10TOPGjT2Kcfr0aR09elRff/21Zs+erb179yooKEj/+Mc/XBW99PR01/jo6Ohy79WwYUMFBQXJbrcrLS3N9Xnx/IrmSs5EUJIyMjJUWFiowMBAj2N7E4keAAAAAI/169ev0jF79+71KMbQoUO1bds21++bNGmi119/XVdccYXrs+PHj7ve16tXr9x72Ww2hYSEKDs721WFKzk/NDS0wrXUrVtXkrNdMycnRxERER7H9iZaNwEAAAA/YjPZ63w6dOhQqd8fPnxY//jHP0olfyV336xVq1aF96tZs6bbnOL3xdfKU/LeBQUFXontTVT0AAAAAHhs0aJFioyM9GmMGTNmKDo6Wrm5uVq3bp1eeeUV7d69W4MHD9asWbN01VVXlbsBSlV5Mt/T2N5EogcAAADAY5GRkR4/g1eZiy++WJIUERGhfv366fLLL9ddd92lvLw8TZw4Uf/5z39Uu3Zt1/jKqmXF10tW34rnF1fpypOXl+d6X1yd8zS2N9G6CQAAAPiR6t9ps/SrOsXExLjO0vvmm2+UlZVV6tm4kydPlju3qKhIp06dkiSFh4e7Pi9+9q6iuZJcz9YFBAS4nufzNLY3kegBAAAA8FsljzFIS0tzHbMguT/TV1JmZqbrQPYmTZq4Pm/ZsmWlcyXn84GS1KhRI9lszrTK09jeRKIHAAAAwHT+/e9/Kz4+XiNGjKhw3O83QImMjFRYWJgkKSUlpdx5Jc+/K3muXUxMjCTp4MGDys3NLXd+8b2LD06X5HFsbyLRAwAAAPyMYZKXLx09elTbt2/Xhg0bdOTIkXLHbdq0SZIUEhLiqqh17dpVkpScnCyHw1HmvPXr10tynmnXunVr1+fFc8+cOaPk5OQy5x4+fNh1sHrnzp1LXfMktjeR6AEAAAAwneJn7woLC/Xqq6+WOWblypXavHmzJKlPnz6qUaOG670k7d+/X/Pnz3ebl5KSoqVLl0qSBg0aVOpZw2bNmqljx46SpClTprg9a+dwODRhwgQVFRUpPDxcvXr1KnXdk9jeRKIHAAAAwHQ6dOig3r17S5KWLVum4cOHa/v27crKytIPP/ygiRMn6umnn5YkNW/eXImJia65119/vbp16yZJGj9+vCZNmqSDBw8qMzNTixcvVkJCgux2u6KjozVgwAC32KNHj5bNZtOBAwcUHx+vzZs3KysrS7t27VJiYqJWrVolSUpMTFRwcHCpuZ7G9hbDUV49EV6VkZHhKuN+tHqDGvl461kAOF8aXvdodS8BALwioChPUXnONsCNGzf6/KiAs1Hye8m//WuRwur79ry6qso+dlQvDusnyTdfs4KCAj355JNavXp1uWPatGmjqVOnKjo6utTnJ06c0ODBg7Vjx44y5zVo0EDz589X8+bNy7y+ZMkSjR07VoWFhWVeT0hI0KhRo8q85mlsb+AcPQAAAACmVKNGDU2ZMkVr167VokWL9N133yknJ0d16tRRmzZtdNttt6l3794KCgpymxsaGqoFCxZowYIFWrFihVJTU1VQUKCoqCjFxcVp6NChql+/frmx+/btq3bt2mnGjBnasmWLjh07puDgYLVv317x8fHq0aNHuXM9je0NJHoAAAAATK1Hjx4VJlblCQoK0sCBAzVw4MBzituqVStNnDjxnOZ6GttTJHoAAACAH7HJkM3ne15WjVnWAXckeoDJrP08RfNWfKHtOw/oWHauagQFqmV0Q930h3Ya2r+LGoTXdY396dAxdez7f2d1/xuuvFTL3nZ/pmpl8rd6d/kX+u/un3Ti5C9qEF5HN1x5qYb276qO7Vt4+scCYFFdronR0H5ddXVsC0WEhij3VL52/JCmBR9u0Xsff1Vqa/FmTSL03fLnz+r+m7f/oDuGv+FR3N8LrVtbw++J0203dlDL6IYKsBlKP5Kt5K17NHXeOv0v/dhZrREAzIhEDzCJwsIzSnxhnhZ/sq3U5wX2M9rxfZp2fJ+mpKWfa87EIbomtuU5x6kbUqvU73/JK9Bf/i9JH274ttTnhzNP6P3V2/X+6u0a9dBtevLBW845JgBrev7R3kq8v3QrVURYoLpe00pdr2mlfj2v0X1P/1t5+fZzjnHyVJ5X48a0aKT3pzyi6MYRpT6/tHmkLm0eqXtuu1ZDx8zSqk07z3nNAGAGJHqASbzw1gpXkvenLrEacV93Xdq8kY78fEJrP0/RKzNXKfP4Sd375L+08d1RahIZpmZNIvTj+pcrvG9RUZEGPPEvbf1uvxpG1NVLT95V6vpT/1zoSvKuiW2pUQ/dqvYx0crMytHsJZ9p+qJPNeHfKyU59OSDPX3yZwfgf+6783pXsrX1ux81ftoK7U49rKaRYXrk3m7q1/Madb++jSY+3U+PjnOeI3XwcJaiuzxR4X0DbDYtfP1hXXfFJTpyLEd/ffk9j+MWqxEUqPmvDlN04wj9klegV2d+omXrv9EveQX6Y8cY/SOxlxo3CNXMFx9U53snKPWno976cgFeZRjOlxmYZR1wxzl6ksaNG6dWrVppyZIl1b0UXKAyMk/o3wuTJUl33XK15k4cqk4dLlZEaIjaXNJUiff30LK3HlVggE3Hc07r9blrJEmGYahOcM0KX1PfXaet3+2XzWbo7X8MVLMmv/0U+6sdP+q9j7+SJMVd11rL3n5UXa5ppYjQELVq2UQvPXmXnnvEeVjppNmr+aYHgMvjD9wsSUrZd0h3PvyGPv3qe2VmndS3ew7qobFztGiV878t995xnZo0DHXNO/VLQYWvxPt76LorLtGZM0Ua9twcHcw47pW4ktS7x5W65CLnlvSPvbhAr876RPv+d1TpR7K18KOt6jtiqn7JK1DtWjU04r7uvvnCAcB5csEnemvXrtW8efOqexm4wH208TsVnimSJP1t+O1ljrmizUW69cYOkqS1n+2q0n237fxRk5PWSpKG9u+qrp1albq+6NckLygwQJNGD1BQYIDbPR65t5uaN62v/IJC/es/yVWKC8DawuoF6+JmDSVJCz/aqvwC9zOmZi52nkVms9l0VbuqnRN1dfsWemzQTZKkfy1M1sate70a96q2zt+fPJWnRau2uc3dnXpYW77b71xLFdcMAGZ1QSd669ev12OPPaaioqLqXgoucBk/n1DtmkFqGFG3VMXt91pGN3SNr0xh4Rk9+dJCnTlTpGaNIzR62G1uY77dc1CSdGXb5opqFF7mfWw2m7pc40wQ132RUmlcANZXVPTbRidl/YBIkuyFZ8ocX56AAJte/9sABQYG6KdDxzT+7Q+9Hrfo1w1aAgNs5babFf46/0wV1gxUF8Nkv2BOF2SiV1RUpMmTJ+uRRx6R3X7uD4gD3vK34bfrp42vasuiMRWO+zEtU5IUWje40nvOXfq5UlIPSZL+nthLIbVruo05nnNaktSscdlJXrH6YXUkST8dztLJU79UGhuAteXk/qJ9/3O2cv/5lo6qEeT+yP99d14vScovsOvrXf+r9J4P9PmD2l0WJUl6bvJSnc4r8Hrc4t/XrlVDd91ytdvcltENdN0Vl0qStny7v9I1A4CZXXCJ3qZNm9SrVy+9+eabKioqUrt27ap7SYBL3ZDa5V7LyDyh1ZudLZvXXn5xhff5Ja9Ar8xYJcnZqtSr+5VljqsT7Ez+ck/nV3i/EydPu94fPlp5NRGA9f3fm8t05kyR2lzSVB9MHaE/drxMDcLrqP1lUZr0twF6oO8fJUmvzPxER47lVHivWjWD9MyQP0mStu08oGXrvvFJ3A/Wfq2vU5zJ3muj79Gj9/fQJRdFqknDUPXucaWWvpmoOsE1lZaRpUmzV5/z1wYAzOCC23VzyJAhkpwn1Q8fPlx33nmnbrrppmpeFVAxh8OhJyb8x7VV+OC7Olc4fuFHW5V5/KQk6bEHyv/33ebSptrxfZq2frfftQFBWT77+gfX+7K2Ogdw4flww7e6/5l39PyjfXTDVZdqxbSRpa6nZWRp3FsrtPDXZ4ErMuC2axVZv54k6bVZn/gs7pkzRerzlyka93hf3XPbtfq/R3vr/x7tXWrMwo+26vk3l1epRR6oLobMs9ulSZaBMlxwFT3DMHTzzTdr2bJlGjFihGy2C+5LAD809o0PtObXDVj+fHNH/bFjTLljHQ6Hpv26aUqbS5qoZ+fYcscWV/qO55zWuLdXlDkmaenn+v7AEdfvC0o8/wLgwlY3pJZO/VJ2R0CD8Lq69vJLXK3fFXk4Pk6ScyfNjz/d4dO4oXVry24/owK7+0YukhQbE13lzWMAwMwuuIrexx9/rJYtz/2waeB8cjgceu6ND1y7Xba9pKleGXVPhXPWfLbLdQxC4n09ZFTwI7+b/9BOXa6O0afbvte/F25Uxs8n9Eh8d13crKEys05qwcotenPeOjVpGKrDmc6fbtcoZwMEABeWCU/epWH33ChJmrH4U/3rPxt1IP1n1Q+rozu6XaExD9+uhD//UddfeYnufHiyMrNOlnmfW/7YXpc1byRJeuPXo2N8Fbf1xY31wZuJatwgVD+mZeofU5Ypeete5RfYdXnrZnp6cE/1uKGdkiYO1V9fWaR/L9zo2RcJAKrRBVfOIsmDvyiwF+qR/0tyVediWjTSe5P/4nqurjzFh67XCa6pO7pdUWmcd8Yn6JrYFpKk5ev+q1sGv6rLbh6lG+4ZrylJa3VV2+Ya//ifXeNDKokPwPq6dmrlSrb+MWWpnvrne/rhf0dkLzyjjJ9P6J33Nuq2h15X7ul8tb64icb+5Y5y79Wvp3NTlJzcXyp8Ns8bcV8bdY8aNwjVwcNZuinhVS1f/1/l5P6i/IJCbf3uR/Ub+bb+s3KLJGncY311UdP65/gVAnzLJsNUL5jTBZfoAf7g+IlTuuvRN13nPF3eupmWvf2oGv36DEt58gvsWvu58wiEW7t2UK2aQZXGiggN0bK3R2rCU3fpqrbNFVy7huqG1NLV7Vvo5Wf668N/PaYzJY4gqWwNAKxvYO8bJDmfh5vy7royx+z8IV2zlmyWJN1z27WqXcZ/j2oEBeqmPzg3Rfto43dlnovnrbgxLRrp+iudO2q+MnOVjmXnljl/9GvvK7/ArqDAAA3sdUOF6wEAM7vgWjcBs/sxLVMDnviXq/2y23VtNOPFByut5EnSxq++d22W0vumq6ocMygwQIPv6qLBd3Up8/quH5zHNDQIr6Pw0JAq3xeANV16UaQk6asdByo8I++z7T8o8b7uCgoMUMvohq4jX4rd2KmV6tVx7ja8ZPV2n8a95Ne5UsVHJ2TnnFbKvsO6su1FuqxFo0rXBABmRaIHmMie/YfV55Ep+vm48yfN9/e6QROf7qfAKj4Xt/6L3ZKk4No1dGOn1lWac+ZMkXJyf6kwgUveukeS86gGAAj69fy6mjWq/m1EjTLGdr++rSTnES8btuzxadySZ+6Vdf5eWWpWcRxw3hnm2XWTzk3zonUTMIkD6T/rz4lvupK8UQ/dptdG31PlJE+StnyXKknq0KqZgqowL2np52ra+XHF3jFWueUcm7Dzh3T9d/dPkqRbb+xQ5bUAsK59/3PuxHvd5ZdUmDRdf8UlkiR74RntP5jpdv26K5xngn6356AKzxS5Xfdm3OK5krOSWJ56dWqrzSVNJEnfH8iodE0AYFYkeoAJ2AvPaOiY2Tr66+G+LzzWR08+eMtZ3aPAXqg9qYclVb3ydk2Hlioqcii/oFBJyz53u/5LXoGemvAfSVLjhqH6881Xn9WaAFhTcZtlRFiIxpSz0Uqrlo314K9nfq75bJdycn8pdT0oMEBtLmkqSdr+6yHmvoy7a98h7d7v/G/kyEE3qVnj8DLnj3usj2rVDFJRUZFrcysA8EckeoAJzPngM1fVrFf3K3XfnTco93R+ha/fS/3pqOsn4i2jG1QpbuuLm6jHDc7WqfHTPtTkuWu0/2Cmjh7L0SebdujWhyZp+67/yWYz9Opf767S5i4ArG/p2m+08au9kqTE+7przj+H6IYrL1FEaIiaNYnQ0P5d9fE7j6tuSC2dOHlaz01e6naPS5tHujoPfiyj2ueLuH99eZEKC8+oflgdrZvzjIb066LmUfUVVi9YN1x5iRa8Nkz3/7oBy+wln+m7vWnn+BUCfMswzPWCOdF8DpjAvxYmu94vW/dNpVuMS1Lml5NL/f5gxnHX+9C6tasce/KYe9XnkSna+2OGXnhrhV54q/TB6bVqBumVv96tm//Yvsr3BGB9A5+ZrtkTBivu2ta6s9sVurOM41yOHMvRoGemuzaXKqlZ4wjX+xMnf3G77ou4m7Z9r6FjZ2vK2PvUMKKuXn6mf5kx3l3+hZ55ZVGV1wQAZkSiB1SzY9m5OpD2s8f3yTl52vW+eBe7qmgYUVerZz2lfy9M1rJ13yj1f0d1pqhI0Y0jFHdtGw0fcKNaRFWtQgjgwpGT+4v+nPim7oi7XPfcdq2ubHuRIkJD9Etegfb9dFQff7pD0xd9Wm4SV/IHUidyT5c5xhdxl679Rlu/+1HD7u6q7te31UVN66tGUIB+Pp6rLd/u1+wPPtOmbd+f3RcDAEyIRA+oZvXD6rhV587FXT2v0V09rzmnucG1auixQTfrsUE3e7wOABcOh8Oh5ev/q+Xr/3vWcxet2uY6K/R8xpWkQ0ez9fcpy/T3KcvOaT5Q3Yxff5mBWdYBdzyjBwAAAAAWQ6IHAAAAABZzwbduRkdHa+/evdW9DAAAAKBKbIbzZQZmWQfcUdEDAAAAAIsh0QMAAAAAi7ngWzcBAAAAf8Kum6gKKnoAAAAAYDEkegAAAABgMbRuAgAAAH7EMJwvMzDLOuCOih4AAAAAWAyJHgAAAABYDK2bAAAAgB9h101UBRU9AAAAALAYEj0AAAAAsBhaNwEAAAA/YhiSzSQdk+y6aV5U9AAAAADAYkj0AAAAAMBiaN0EAAAA/Ai7bqIqqOgBAAAAgMWQ6AEAAACAxdC6CQAAAPgRwzDPbpdmWQfcUdEDAAAAAIsh0QMAAAAAi6F1EwAAAPAjxq8vMzDLOuCOih4AAAAAWAyJHgAAAABYDK2bAAAAgB+xGZLNJNtd2syxDJSBih4AAAAAWAyJHgAAAABYDK2bAAAAgB9h101UBRU9AAAAALAYEj0AAAAAsBhaNwEAAAB/Qu8mqoCKHgAAAABYDIkeAAAAAFgMrZsAAACAHzF+/WUGZlkH3FHRAwAAAACLIdEDAAAAAIuhdRMAAADwI4YkwyQdkyZZBspARQ8AAAAALIZEDwAAAAAshtZNAAAAwM/QMonKUNEDAAAAAIsh0QMAAAAAi6F1EwAAAPAnhszTu2mWdcANFT0AAAAAsBgqegAAAABMb+PGjXr//ff13//+V1lZWapRo4aaN2+url27auDAgYqIiChznt1u14IFC7R8+XKlpqbK4XAoKipKPXr0UEJCgsLCwiqMu3fvXk2fPl1btmxRVlaWwsLC1L59e8XHx6tLly4VzvU0tidI9AAAAAA/Yvz6ywzOxzoKCws1atQorVixotTndrtdKSkpSklJ0Xvvvac333xTV155Zakx+fn5GjJkiLZu3Vrq83379mnfvn1asmSJZsyYoZiYmDJjr1u3TiNHjpTdbnd9lpmZqQ0bNmjDhg26//77NWbMmDLnehrbU7RuAgAAADCtV1991ZXkde/eXQsWLNCXX36pFStW6KmnnlJwcLCOHTum4cOH68iRI6Xmjh49Wlu3blVQUJAef/xxrVu3Tps2bdK4ceMUGhqqo0ePavjw4Tp9+rRb3JSUFD3xxBOy2+2KjY1VUlKSvvzySy1evFg9evSQJCUlJWnevHllrtuT2N5AogcAAADAlI4cOaK5c+dKku644w699dZbuuqqqxQeHq6YmBgNHTpUc+fOVWBgoLKzs/Wvf/3LNXfHjh1auXKlJOnZZ5/V8OHDFR0drcjISPXr10+zZ89WUFCQ0tPTXTFKeuONN5SXl6fmzZtrzpw56tSpk8LDwxUbG6upU6eqZ8+ekqTJkycrNze31FxPY3sDiR4AAADgRwzDXC9fWrt2rQoLCyVJjz/+eJljYmNjXRW25ORk1+ezZs2SJEVHR6t///5u89q2bavevXtLkhYtWlTqWmpqqutew4YNU0hISKnrhmFo1KhRstlsys7O1po1a0pd9yS2t5DoAQAAADClo0ePqlatWmrQoIGioqLKHde8eXPXeElyOBzatGmTJCkuLk4BAQFlzuvevbskKS0tTXv27HF9XjzXMAzFxcWVObdJkyZq06aNJGdCWszT2N5CogcAAADAlB5//HF9++23+uSTTyoc97///U+SFBoaKsmZPOXk5EiS2rVrV+68tm3but7v3LnT9X737t2SpKZNm5a7m2fJ+bt27XJ95mlsb2HXTQAAAMCPmPW89OJqWkUaN258TnHq1KlT7rUjR45ow4YNkqSOHTtKktLT013Xo6Ojy53bsGFDBQUFyW63Ky0tzfV58fyK5krORFCSMjIyVFhYqMDAQI9jewuJHgAAAACP9evXr9Ixe/fu9WpMh8Oh5557Tvn5+ZKk+Ph4SdLx48ddY+rVq1fufJvNppCQEGVnZ7uqcCXnF1cIy1O3bl3XOnJychQREeFxbG+hdRMAAACAX3rppZdcm6bcfvvtuu666yTJlfhJUq1atSq8R82aNd3mFL8vvlaekvcuKCjwSmxvoaIHAAAA+BOT9m4uWrRIkZGR5yWsw+HQhAkTNGfOHElSTEyMnn/+edf18jZAqSpP5nsa21tI9AAAAAB4LDIy8pyfwTsbBQUFevbZZ7V8+XJJ0iWXXKKZM2eWOgKhdu3arveVVcuKr5esvhXPL67SlScvL8/1vrg652lsbyHRAwAAAOAXsrOzNWLECH311VeSnLtaTp8+3W1nzJLPxp08ebLc+xUVFenUqVOSpPDwcNfnxc/eVTRXkuvZuoCAANfzfJ7G9hae0QMAAAD8iGGyX+fLTz/9pLvvvtuV5HXu3FlJSUllHn/QokUL1/tDhw6Ve8/MzEzZ7XZJznPxirVs2bLSuZJ0+PBhSVKjRo1ks9m8EttbSPQAAAAAmNoPP/ygu+++WwcOHJAk9e/fX9OmTSvVrllSZGSkwsLCJEkpKSnl3rfk+Xclz7WLiYmRJB08eFC5ubnlzi++d/HB6d6I7S0kegAAAABM6+DBg0pISFBWVpYkaeTIkXrhhRcUGFjxU2hdu3aVJCUnJ8vhcJQ5Zv369ZKcZ9q1bt3abe6ZM2dcu3r+3uHDh10Hq3fu3Nlrsb2FRA8AAADwI4Zhrpcv2e12PfbYY8rMzJQkjR49Wn/5y1+qNLdPnz6SpP3792v+/Plu11NSUrR06VJJ0qBBg2SU+MM0a9bMdfj6lClT3J61K971s6ioSOHh4erVq5fXYnsLiR4AAAAAU1q4cKF27twpSfrTn/6kfv366dSpUxW+il1//fXq1q2bJGn8+PGaNGmSDh48qMzMTC1evFgJCQmy2+2Kjo7WgAED3GKPHj1aNptNBw4cUHx8vDZv3qysrCzt2rVLiYmJWrVqlSQpMTFRwcHBpeZ6GtsbDEd5tUR4VUZGhquE+9HqDWp0HraeBYDzoeF1j1b3EgDAKwKK8hSVt0mStHHjxvNyVEBVlfxe8q35K1W/YaNqXpHTscwj+kv8bZJ88zW76aab9NNPP53VnL1797renzhxQoMHD9aOHTvKHNugQQPNnz9fzZs3L/P6kiVLNHbsWBUWFpZ5PSEhQaNGjSrzmqexPcXxCgAAAIAfMel56V6XlZV11kne74WGhmrBggVasGCBVqxYodTUVBUUFCgqKkpxcXEaOnSo6tevX+78vn37ql27dpoxY4a2bNmiY8eOKTg4WO3bt1d8fLx69Ojhs9ieItEDAAAAYDoRERGlqnPnKigoSAMHDtTAgQPPaX6rVq00ceLEaontCZ7RAwAAAACLoaIHAAAA+JMLpXcTHqGiBwAAAAAWQ6IHAAAAABZD6yYAAADgR4xff5mBWdYBd1T0AAAAAMBiSPQAAAAAwGJo3QQAAAD8iGE4X2ZglnXAHRU9AAAAALAYEj0AAAAAsBhaNwEAAAA/wnnpqAoqegAAAABgMSR6AAAAAGAxtG4CAAAA/oTeTVQBFT0AAAAAsBgSPQAAAACwGFo3AQAAAL9iyDBNz6RZ1oHfo6IHAAAAABZDogcAAAAAFkPrJgAAAOBHDMP5MgOzrAPuqOgBAAAAgMWQ6AEAAACAxdC6CQAAAPgRzktHVVDRAwAAAACLIdEDAAAAAIuhdRMAAADwN/RMohJU9AAAAADAYkj0AAAAAMBiaN0EAAAA/Ijx6y8zMMs64I6KHgAAAABYDIkeAAAAAFgMrZsAAACAHzEM58sMzLIOuKOiBwAAAAAWQ6IHAAAAABZD6yYAAADgRwyZ57x0s6wD7qjoAQAAAIDFkOgBAAAAgMXQugkAAAD4E3o3UQVU9AAAAADAYkj0AAAAAMBiaN0EAAAA/Ijx6y8zMMs64I6KHgAAAABYDIkeAAAAAFgMrZsAAACAHzEM58sMzLIOuKOiBwAAAAAWQ6IHAAAAABZD6yYAAADgRzgvHVVBRQ8AAAAALIZEDwAAAAAshtZNAAAAwJ/Qu4kqoKIHAAAAABZDogcAAAAAFkPrJgAAAOBXDBmm6Zk0yzrwe1T0AAAAAMBiSPQAAAAAwGJo3QQAAAD8iGE4X2ZglnXAHRU9AAAAALAYEj0AAAAAsBhaNwEAAAA/wnnpqAoqegAAAABgMSR6AAAAAGAxtG4CAAAA/oTeTVQBFT0AAAAAsBgSPQAAAACwGFo3AQAAAD/i7Nw0R8+kOVaBslDRAwAAAACLIdEDAAAAAIuhdRMAAADwI4bhfJlBda1j3LhxSkpK0ksvvaS+fftWONZut2vBggVavny5UlNT5XA4FBUVpR49eighIUFhYWEVzt+7d6+mT5+uLVu2KCsrS2FhYWrfvr3i4+PVpUsXn8b2BIkeAAAAAL+xdu1azZs3r0pj8/PzNWTIEG3durXU5/v27dO+ffu0ZMkSzZgxQzExMWXOX7dunUaOHCm73e76LDMzUxs2bNCGDRt0//33a8yYMT6J7SlaNwEAAAD4hfXr1+uxxx5TUVFRlcaPHj1aW7duVVBQkB5//HGtW7dOmzZt0rhx4xQaGqqjR49q+PDhOn36tNvclJQUPfHEE7Lb7YqNjVVSUpK+/PJLLV68WD169JAkJSUllZt0ehLbG0j0AAAAAD9imOx1PhQVFWny5Ml65JFHSlXXKrJjxw6tXLlSkvTss89q+PDhio6OVmRkpPr166fZs2crKChI6enpmjt3rtv8N954Q3l5eWrevLnmzJmjTp06KTw8XLGxsZo6dap69uwpSZo8ebJyc3O9GtsbSPQAAAAAmNamTZvUq1cvvfnmmyoqKlK7du2qNG/WrFmSpOjoaPXv39/tetu2bdW7d29J0qJFi0pdS01NVXJysiRp2LBhCgkJKXXdMAyNGjVKNptN2dnZWrNmjddiewuJHgAAAADTGjJkiL7//nsFBQUpMTFRr7/+eqVzHA6HNm3aJEmKi4tTQEBAmeO6d+8uSUpLS9OePXtcnxfPNQxDcXFxZc5t0qSJ2rRpI8n53KC3YnsLiR4AAADgR4p33TTLy/d/XkM333yzli1bphEjRshmqzyFSUtLU05OjiRVWAFs27at6/3OnTtd73fv3i1Jatq0qSIiIiqdv2vXLq/F9hZ23QQAAABgWh9//LFatmx5VnPS09Nd76Ojo8sd17BhQwUFBclutystLc1tfkVzJWciKEkZGRkqLCxUYGCgx7G9hUQPAAAAgMeOHj1a6ZjGjRuf9X3PNsmTpOPHj7ve16tXr9xxNptNISEhys7OdlXhSs4PDQ2tME7dunUlOds1c3JyFBER4XFsbyHRAwAAAPzK+dzvsjK/raNfv36Vjt67d68vF+OSn5/vel+rVq0Kx9asWdNtTvH74mvlKXnvgoICr8T2FhK986SwsND1/ufMzGpcCQB4V0BRXnUvAQC8wub47Zvtkt+7wf+UtwHK+ZjvaWxvIdE7T7KyslzvB97rvsUqAPirqOpeAAD4QFZWVqXPZ6G0RYsWKTIysrqXIUmqXbu2631l1bLi6yWrb8Xzi6t05cnL++2HncXVOU9jewuJHgAAAOBHDJ2f3S6rouQyIiMjz+kZPF8o+WzcyZMnyx1XVFSkU6dOSZLCw8Ndnxc/e1fRXEmuZ+sCAgJcz/N5GttbSPTOk5iYGNdhiBEREQoM5EsPAABgJoWFha4urJiYmGpeDTzRokUL1/tDhw6pY8eOZY7LzMyU3W6X5DwXr1jLli21detWHTp0qMI4hw8fliQ1atTIdeyDp7G9hWzjPKlVq5Y6dOhQ3csAAABABWjXtIbIyEiFhYUpOztbKSkpuuOOO8ocV/L8u5Ln2hUn+gcPHlRubq7q1KlT5vyUlBRJch2c7o3Y3sKB6QAAAIAfMUz2MquuXbtKkpKTk+VwOMocs379eknOM+1at27tNvfMmTNKTk4uc+7hw4ddB6t37tzZa7G9hUQPAAAAgOX06dNHkrR//37Nnz/f7XpKSoqWLl0qSRo0aJCMEg8+NmvWzNVyOWXKFLdn7RwOhyZMmKCioiKFh4erV69eXovtLSR6AAAAACzn+uuvV7du3SRJ48eP16RJk3Tw4EFlZmZq8eLFSkhIkN1uV3R0tAYMGOA2f/To0bLZbDpw4IDi4+O1efNmZWVladeuXUpMTNSqVaskSYmJiQoODvZqbG8wHOXVEgEAAACYQkZGhqsd8L0VaxTZyBy7Wx49kqH+d9wkSdq4ceN52XUzLS1N3bt3lyS99NJL6tu3b7ljT5w4ocGDB2vHjh1lXm/QoIHmz5+v5s2bl3l9yZIlGjt2bLnnKiYkJGjUqFE+ie0pNmMBAAAAYEmhoaFasGCBFixYoBUrVig1NVUFBQWKiopSXFychg4dqvr165c7v2/fvmrXrp1mzJihLVu26NixYwoODlb79u0VHx+vHj16+Cy2p6joAQAAACZHRQ9ni4oeAAAA4EeMX3+ZgVnWAXdsxgIAAAAAFkOiBwAAAAAWQ+smAAAA4E/MdFK5WdYBN1T0AAAAAMBiSPQAAAAAwGJo3QQAAAD8CJ2bqAoqegAAAABgMSR6AAAAAGAxtG4CAAAAfsQwnC8zMMs64I6KHgAAAABYDIkeYBF79+7V008/rS5duqh9+/b64x//qOHDh+vTTz+t7qUBgFeMGzdOrVq10pIlS6p7KQBgerRuAhawbt06jRw5Una73fVZZmamNmzYoA0bNuj+++/XmDFjqnGFAOCZtWvXat68edW9DMAUjF9/mYFZ1gF3VPQAP5eSkqInnnhCdrtdsbGxSkpK0pdffqnFixerR48ekqSkpCS+QQLgt9avX6/HHntMRUVF1b0UAPAbJHqAn3vjjTeUl5en5s2ba86cOerUqZPCw8MVGxurqVOnqmfPnpKkyZMnKzc3t5pXCwBVV1RUpMmTJ+uRRx4p1bEAAKgciR7gx1JTU5WcnCxJGjZsmEJCQkpdNwxDo0aNks1mU3Z2ttasWVMNqwSAs7dp0yb16tVLb775poqKitSuXbvqXhJgHobJXjAlEj3Aj23atEmSM6GLi4src0yTJk3Upk0bSc5nXADAHwwZMkTff/+9goKClJiYqNdff726lwQAfoVED/Bju3fvliQ1bdpUERER5Y5r27atJGnXrl3nZV0A4CnDMHTzzTdr2bJlGjFihGw2vmUBgLPBrpuAH0tPT5ckRUdHVziuadOmkqSMjAwVFhYqMJD/6QMwt48//lgtW7as7mUApmSmjkmzrAPu+PEY4MeOHz8uSQoNDa1wXN26dSVJDodDOTk5Pl8XAHiKJA8APEOiB/ix/Px8SVLNmjUrHFerVi3X+4KCAp+uCQAAANWP/i3AjwUEBFT3EgAAwHlmGM6XGZhlHXBHRQ/wY7Vr15ZUeZUuLy/P9b6y6h8AAAD8H4ke4MeKn707efJkheOKn8sLCAio9Hk+AAAA+D8SPcCPFW9WcOjQoQrHHT58WJLUqFEjtigHAMDPGSb7BXPiOz7Aj8XExEiSDh48qNzc3HLHpaSkSJLr4HQAAABYG4ke4Me6du0qSTpz5oySk5PLHHP48GHXweqdO3c+X0sDAABANSLRA/xYs2bN1LFjR0nSlClT3J7VczgcmjBhgoqKihQeHq5evXpVxzIBAIAXFe+6aZYXzIlED/Bzo0ePls1m04EDBxQfH6/NmzcrKytLu3btUmJiolatWiVJSkxMVHBwcDWv9v/bu/+gqquEj+Pvi4CKG6KBo0auha6iKKuZZpauyWjqbrkGmiSuVopZudNq5ea6rZtsDJUUYK26sqGMGy5mJLqtkhmIiD9QWMSQUTO8KQrID3+ABDx/0L0P18vlh/IseJ/Pi2GGOd/vOZzzhSk+nvM9R0RERET+G3SOnsgdbsiQIYSEhLBixQpOnjzJc889Z3XPvHnzeOaZZ9qgdyIiIiLSFhT0ROzA9OnTGTx4MBs2bCA9PZ2ioiJcXFzw8fEhMDAQPz+/tu6iiIiIiPwXKeiJ2IkBAwYQFhbW1t0QEfk/4enpSW5ublt3Q0TkjqF39EREREREROyMZvRERERERO4k7Wm3y/bSD7GiGT0RERERERE7o6AnIiIiIiJiZ7R0U0RERETkDmL48aM9aC/9EGua0RMREREREbEzCnoiIiIiIiJ2Rks3RURERETuIIZ2tOtme+mHWNOMnoiIiIiIiJ1R0BMREREREbEzWropIiIiInIHMdB+zilvL/0Qa5rRExERERERsTMKeiIiIiIiInZGSzdFRERERO40WjMpTdCMnoiIiIiIiJ1R0BMREREREbEzWropIiI2VVdX06FDh7buhoiI1GP48aM9aC/9EGua0RMRu/XYY48xYMAAm58+Pj6MHDmSadOmsWrVKvLy8tq6y40KCgpiwIABLFu2zKI8MjLSPKbWcuPGDSIjI9mwYUOrtdlc586dM48nPT292fXS09NvqV5TPv30U3O7586da7V2m2PZsmUMGDCAxx577L/6fUVE5M6noCci/29VVVVRWlrKiRMn2LRpE0888QR///vf27pb7cKcOXOIioqisrKyrbsiIiIit0BLN0XE7j3wwAOsX7/eqrympoby8nJSU1N57733uHz5MqGhofTv359HHnmkDXp6a7p27UqfPn1atc2LFy+2ansiItJ6DIa6z/agvfRDrCnoiYjd69ChA126dGnw2l133UVAQABeXl4EBgZSW1tLZGTkHRX05syZw5w5c9q6GyIiItKOaOmmiAgwfPhwRo0aBcCxY8coKipq4x6JiIiI3DrN6ImI/Mjb25sDBw4AYDQaufvuu4G6TVAOHjzIwoULmThxIn/+85/JycnBxcUFHx8fPvroI5ydnQGora1lx44dJCQkcPz4ccrKynBzc8PX15cZM2Ywbty4RvuQnJxMbGwsJ06coKysjD59+jBt2rRGZ+wiIyOJiooCIDc3t8F7UlNT2bJlC9nZ2RQUFNClSxe8vb3x9/fnl7/8pfk+01hNoqKiiIqK4p577mHPnj0WbV6+fJmYmBi++uor8vPzqa6uplevXjz66KM8++yz9OrVy2afr1y5QlxcHImJiZw9exZHR0eGDh3K/Pnzueeeexp9RrcjOTmZxMREjh07RmFhITdu3MDV1ZWBAwcyefJkpk2bhpOTU6NtpKens27dOjIzM6murub+++/nV7/6FYGBgebfg4Z8//33fPzxx6SkpHD+/HkMBgP33nsv48ePZ+7cuXTr1q21hysidspA+zkvvb30Q6wp6ImI/MhQ70WDho4UyM/P5ze/+Q3l5eVA3c6UgPmP+7KyMl566SWrHR8vXbpEUlISSUlJPPHEE4SEhFgFgurqalauXElcXJxF+cmTJwkLC2Pv3r23tDFKZWUlb775Jtu2bbMoLykpIS0tjbS0NL744gvCw8ObDDj1HThwgMWLF1NaWmpRfubMGc6cOcOWLVsICwtj0qRJVnXz8/N5/vnn+fbbby3KU1JS2LdvH/PmzWv+AJvp+vXrvPLKK3z11VdW14qKikhNTSU1NZXExESio6NtHimxefNmoqOjqa2tNZdlZ2eTnZ1NfHw80dHR9OjRw6rejh07+P3vf2/1M8zNzSU3N5dPPvmENWvWMGLEiNscqYiISB0t3RQR+dF//vMfABwdHfnpT39qdX3Hjh3U1tbywQcfsH//fj7++GMWLVoE1AW1F198kfT0dBwdHZk/fz6JiYmkp6eTkJDA7NmzMRgMfP7556xatcqq7Q8//NAc8vz8/PjnP//JgQMHiIuL4xe/+AUHDx4kMzOzxWMKCQkxh7xJkyYRFxfHgQMH2LZtG1OnTgVg9+7drFmzBoD169eTkZFB7969AQgODiYjI4MdO3aY2zx58iTBwcGUlpbi6elJWFgYycnJpKWlsW7dOnx8fKioqOB3v/sdR44csejPjRs3zCGvU6dOvPrqq+zZs4fU1FTCwsLw8PAgOjq6xeNsyjvvvGMOebNnz2bbtm2kpaWxe/du3n//ffr16wfUBdjt27fbbGfDhg306NGDd999l/3797Nr1y4WLFiAg4MDeXl5vPzyy9TU1FjUSU1NZenSpVRWVjJw4EDWrFnD/v37SUlJITw8nL59+1JSUsKCBQuswq+IiMit0oyeiAh1f4wfOnQIgDFjxvCTn/ykwfuWLVvG448/DsDo0aPN5du2bTMveQwPD2fixInma25ubqxYsQJPT09CQ0OJi4tj5syZDB48GICCggL+9re/ATBlyhRWr15tnl3s1q0bH330Eb/97W/ZtWtXi8aUnZ3Nli1bAAgMDOTNN980X+vWrRurV6+moqKCL7/8ko0bNxIcHEznzp2B/53ddHJystrIZuXKlVRUVODp6Ul8fLzFksNx48bx0EMPMXv2bLKysli5ciWff/65+frmzZvNYSYiIsJiKeuTTz7JAw88wK9//WvKyspaNNbGlJeXm59DQEAAK1asMF/r3r07ffr0YcSIEfj5+VFRUUFKSgrTpk1rsC03Nzc2b96Mp6cnAHfffTdLlizBw8ODkJAQjh07xr///W8mT54M1P0DwIoVK6ipqWHo0KHExsbSsWNHc3tTpkzh4YcfZvr06RiNRkJDQ/nrX//aamMXETultZvSDJrRExG7V11dzdWrV60+i4uLycrKIjw8nBdeeAGoCzavvPJKg+0YDIYGlyIC/OMf/wDgwQcftAh59c2ZM8f8/pkpeAAkJSVRUVGBg4MDr7/+usUSUgAHBweWL1+Og0PL/pNtmoF0cXFh6dKlDd4THByMl5cXo0ePprCwsMk28/LyOHz4MACLFi1q8L2yjh07mp9hbm6uxUykabZszJgxDb6v6Onpyfz585seXAuUl5czd+5cJk+ebHNZqIeHB/fddx8AxcXFNtsKDg42h7z6goKCzEdcfPbZZ+bylJQUjEYjAEuWLLEIeSZubm7m37+9e/dy6dKl5g1MRESkEZrRExG7d+TIEYYPH97kfZ07dyYsLAxvb+8Gr3t6euLq6mpVfuXKFXJycgAYNGgQV69etfk9hgwZgtFoJCMjw1xm2gCmf//+9OzZs8F6PXv2ZMiQIS1avmlqd9SoUTaPl/D19WXnzp3NbrP+Ri0/+9nPbI514MCBdOjQgerqao4cOYKvry/l5eUcP34cgLFjx9r8HhMmTOC9995rdp+a0rt3b5tBF+qWk2ZlZXH9+nUAfvjhB5v32grxBoOBsWPHEhsby5EjR6itrcVgMFi8r9nY8/Lx8QHqNvPJyMiw+Q8KIiIizaWgJyL/b3Xs2BFXV1e8vLwYOXIkAQEBDW6kYdK9e/cGy41Go/m9rJiYGGJiYpr83ufPn7f6uqH3Auvz8vJqUdArKCgAoG/fvs2u05T8/Hzz1/7+/s2qYxpfQUGBeROTxg54v++++8whsbWdOnWKw4cPc+bMGb777jvOnj3Lt99+22i4M3F2dm5wNs/E9PMrLy+nrKyMrl27cu7cOfP1+kt9G1P/d0NEpCGGHz/ag/bSD7GmoCcidm/kyJFs2rTptttpaNkd1M3otVT9OqZdPDt16tRoHVvvDdpi2hGzqXZb4nbGWv+9O9O7gA1xcHDAxcXF/Fxag9Fo5I033jDPctbn5ubGqFGjyMnJsQiyN2uszzdfr6iooGvXrrf9uyEiInKrFPRERG5T/T/w//SnPzFr1qwW1e/atSuAeemgLabjHFrSr/LycioqKlpUrzH1Q2NWVpbN8NsQ0zgBrl271ui9LR1rY8rKyggKCsJoNOLg4MDYsWMZMWIE/fv3x8vLi3vvvReAWbNmNRr0mnqO9Zdlmpb4mp6Xu7s7qamptzsUERGRZlPQExG5TfXfqzNtvGGL6d2t+nr37k1mZianT59utG79ZYDN0atXL8rLy/nuu+8avW/16tW4u7vz8MMPm48ZsMV07IKpP15eXjbvvXmsPXv2xMHBgZqaGk6fPs2ECRMarHfx4sVbOjPQls2bN5t/Lh988IHN9+wuX77caDuVlZUUFhbi7u7e4HXTz8/Dw8Mc/k3P6/Lly1y7dg0XF5dbGoOISH0GQ91ne9Be+iHWtOumiMht6t69uzkg7dmzx+Iw7fpqamqYOnUqjz76KK+++qq53LQxyalTp2yGvStXrlhs4NIcw4YNA+DQoUM2g1NeXh5r164lJCSE3NzcJtusf6D3l19+afO+jIwMfH19mTRpEv/6178A6NKli7l+Y3WTk5Ob7EdLHD16FKg7UsJWyLtw4YL52Iebz8Grb9++fQ2WV1VVsWfPHsDyGZm+rq6uZu/evTbb3b59O8OGDWPq1KnmXU1FRGy5dOkiBRcutIvPS5cutvXjEBs0oyci0goCAgJ4++23OXXqFBs2bOD555+3umfjxo2cOnUKwGLmzM/Pj9DQUEpLS1m1ahVr167FycnJom54eHiTyx1v9tRTTxEXF0dZWRlRUVEsWbLE6p6IiAgAXFxcLI47cHSs+99DVVWVxf1Dhw7F29ubEydOsH79eiZOnGi12UtFRQWhoaFUVlZiNBoZOnSoRZ8OHjzI0aNH+fTTT5k+fbpF3ZKSEj788MMWjbMpHTp0AOreWbx06RIeHh4W1ysrK1m+fLk5oN885voiIyMZP368xTJUU7npWISZM2eayydMmIC7uzuFhYW8++67PPTQQ1ab+hQXFxMREcG1a9coLCy0ueuriIjJM08HtHUX5A6gGT0RkVYQGBjIoEGDAHjnnXd44403yM7OpqSkhNzcXN5++21CQ0OBul0wg4KCzHVdXV157bXXgLqD2+fNm8fhw4cpKSnhm2++4bXXXiM2NtYcWJrL19fXfPD3unXrWL58OSdOnODy5ctkZmayePFi8yHsL7/8ssVmL25ubkDdOXAFBQUWZ8v98Y9/xNHRkbKyMmbOnElsbCznzp2jqKiIffv2MXfuXPPuoM8995z57ECoOxT9wQcfBOAPf/gDq1ev5uzZsxQXF5OUlMTTTz/N+fPnrZa33o5HHnkEqJupCw4OJi0tjaKiIvLz80lISMDf399ips7WEQgGgwGj0UhgYCBff/01xcXF5OXlsWLFCtauXQvA1KlTLXbXdHZ2Zvny5UDdsl5/f38+++wzCgoKKCgoYNeuXQQFBZmX1y5ZssTmURgiIiItoRk9EZFW4OzszLp161i0aBFZWVls3bqVrVu3Wt3Xt29f1q9fb/Wulr+/P0VFRYSHh3Po0CGeeeYZi+uDBw+mX79+JCQktKhfK1eu5OrVq+zevZv4+Hji4+Ot7gkKCuLZZ5+1KBs1ahSZmZnk5OQwduxYnJycOHr0KE5OTgwfPpyIiAiWLl1KSUkJb731Fm+99ZZVuwEBASxevNiizGAwEBUVxcKFCzl69Chr1641hySTpUuXEhER0Wobsvj7+7Nz504OHTrE8ePHmTt3rtU9999/P4MGDSIxMRGj0UhVVZXVrGrnzp1ZsGAB77//PgsWLLBqY9y4cYSEhFiVT5kyhbKyMlatWoXRaOT111+3usdgMPDiiy8yY8aMWx+oiNg1d3d3vv7667buRqNsvcMsbUNBT0SklXh4ePDJJ5+wfft2EhMTycnJobS0lE6dOtG/f38mTZrErFmzbB53EBwczOjRo4mOjiYzM5PCwkJ69erFlClTWLhwIX/5y19a3KdOnToRFRVFUlIS8fHxZGVlUVpayl133cXPf/5zgoKCGDNmjFW9l156ievXr/PFF19QUlJC9+7duXDhgnmHygkTJrBr1y42bdpEcnIy+fn5VFZW0q1bN4YNG8bMmTMbbBfqZgs3btxIQkICW7du5fTp0/zwww94e3szb948/Pz8zEtKW4OzszPR0dHExMSwc+dOzpw5Q1VVFa6urvTr14/HH3+cp556iuzsbBITE7l+/Tr79u1j/PjxVm298MIL9OvXj+joaL755hsMBgMDBw5kxowZPPnkkzZnIp9++mnGjBlDTEwMaWlpfP/991RVVdGjRw9GjBjB7NmzLZa4iojczNHR0WLzL5GmGGpt7RogIiIiIiIidyS9oyciIiIiImJnFPRERERERETsjIKeiIiIiIiInVHQExERERERsTMKeiIiIiIiInZGQU9ERERERMTOKOiJiIiIiIjYGQU9ERERERERO6OgJyIiIiIiYmcU9EREREREROyMgp6IiIiIiIidUdATERERERGxMwp6IiIiIiIidkZBT0RERERExM4o6ImIiIiIiNgZBT0RERERERE7o6AnIiIiIiJiZxT0RERERERE7IyCnoiIiIiIiJ1R0BMREREREbEzCnoiIiIiIiJ2RkFPRERERETEzijoiYiIiIiI2BkFPRERERERETvzP9I3d//k+QYGAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 408,
       "width": 445
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=5,5 \n",
    "sns.set_style(\"white\")\n",
    "# plot_confusion_matrix(model, X_test, y_test, cmap=plt.cm.Blues)  \n",
    "ConfusionMatrixDisplay.from_predictions(y_test, y_predictions, cmap=plt.cm.Blues)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "id": "fa49264f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:24:03.712110Z",
     "iopub.status.busy": "2022-11-10T04:24:03.711564Z",
     "iopub.status.idle": "2022-11-10T04:24:04.298455Z",
     "shell.execute_reply": "2022-11-10T04:24:04.297378Z"
    },
    "papermill": {
     "duration": 0.701046,
     "end_time": "2022-11-10T04:24:04.301190",
     "exception": false,
     "start_time": "2022-11-10T04:24:03.600144",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 812,
       "width": 906
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=10,10\n",
    "sns.set_style(\"white\")\n",
    "sns.despine()\n",
    "feat_importances = pd.Series(model.feature_importances_, index=feature_names)\n",
    "feat_importances = feat_importances.groupby(level=0).mean()\n",
    "feat_importances.nlargest(20).plot(kind='barh').invert_yaxis()\n",
    "sns.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "823432f8",
   "metadata": {
    "papermill": {
     "duration": 0.118365,
     "end_time": "2022-11-10T04:24:04.537171",
     "exception": false,
     "start_time": "2022-11-10T04:24:04.418806",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id='4_5'></a>\n",
    "## <p style=\"padding: 8px;color:white; display:fill;background-color:#aaaaaa; border-radius:5px; font-size:100%\"> <b>Random Forest</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "id": "0fc90487",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:24:04.774729Z",
     "iopub.status.busy": "2022-11-10T04:24:04.774007Z",
     "iopub.status.idle": "2022-11-10T04:24:10.460329Z",
     "shell.execute_reply": "2022-11-10T04:24:10.458896Z"
    },
    "papermill": {
     "duration": 5.811551,
     "end_time": "2022-11-10T04:24:10.462808",
     "exception": false,
     "start_time": "2022-11-10T04:24:04.651257",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 18 s\n",
      "Wall time: 1.51 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "start = time.time()\n",
    "model = RandomForestClassifier(n_estimators = 100,n_jobs=-1,random_state=0,bootstrap=True,).fit(X_train,y_train)\n",
    "end_train = time.time()\n",
    "y_predictions = model.predict(X_test) # These are the predictions from the test data.\n",
    "end_predict = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "id": "e7979df4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:24:10.665368Z",
     "iopub.status.busy": "2022-11-10T04:24:10.665070Z",
     "iopub.status.idle": "2022-11-10T04:24:10.703854Z",
     "shell.execute_reply": "2022-11-10T04:24:10.702795Z"
    },
    "papermill": {
     "duration": 0.144123,
     "end_time": "2022-11-10T04:24:10.706617",
     "exception": false,
     "start_time": "2022-11-10T04:24:10.562494",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 97.68%\n",
      "Recall: 97.68%\n",
      "Precision: 97.69%\n",
      "F1-Score: 97.68%\n",
      "time to train: 1.47 s\n",
      "time to predict: 0.04 s\n",
      "total: 1.51 s\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_predictions)\n",
    "recall = recall_score(y_test, y_predictions, average='weighted')\n",
    "precision = precision_score(y_test, y_predictions, average='weighted')\n",
    "f1s = f1_score(y_test, y_predictions, average='weighted')\n",
    "\n",
    "print(\"Accuracy: \"+ \"{:.2%}\".format(accuracy))\n",
    "print(\"Recall: \"+ \"{:.2%}\".format(recall))\n",
    "print(\"Precision: \"+ \"{:.2%}\".format(precision))\n",
    "print(\"F1-Score: \"+ \"{:.2%}\".format(f1s))\n",
    "print(\"time to train: \"+ \"{:.2f}\".format(end_train-start)+\" s\")\n",
    "print(\"time to predict: \"+\"{:.2f}\".format(end_predict-end_train)+\" s\")\n",
    "print(\"total: \"+\"{:.2f}\".format(end_predict-start)+\" s\")\n",
    "model_performance.loc['Random Forest'] = [accuracy, recall, precision, f1s,end_train-start,end_predict-end_train,end_predict-start]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "id": "b89d1baa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:24:10.912878Z",
     "iopub.status.busy": "2022-11-10T04:24:10.912530Z",
     "iopub.status.idle": "2022-11-10T04:24:11.426976Z",
     "shell.execute_reply": "2022-11-10T04:24:11.425725Z"
    },
    "papermill": {
     "duration": 0.622469,
     "end_time": "2022-11-10T04:24:11.429534",
     "exception": false,
     "start_time": "2022-11-10T04:24:10.807065",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 408,
       "width": 445
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=5,5 \n",
    "sns.set_style(\"white\")\n",
    "ConfusionMatrixDisplay.from_predictions(y_test,y_predictions, cmap=plt.cm.Blues)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "id": "3925b8d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:24:11.644521Z",
     "iopub.status.busy": "2022-11-10T04:24:11.643731Z",
     "iopub.status.idle": "2022-11-10T04:24:12.246692Z",
     "shell.execute_reply": "2022-11-10T04:24:12.245901Z"
    },
    "papermill": {
     "duration": 0.71233,
     "end_time": "2022-11-10T04:24:12.249417",
     "exception": false,
     "start_time": "2022-11-10T04:24:11.537087",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 812,
       "width": 906
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=10,10\n",
    "sns.set_style(\"white\")\n",
    "feat_importances = pd.Series(model.feature_importances_, index=feature_names)\n",
    "feat_importances = feat_importances.groupby(level=0).mean()\n",
    "feat_importances.nlargest(20).plot(kind='barh').invert_yaxis()\n",
    "sns.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b2578b3",
   "metadata": {
    "papermill": {
     "duration": 0.120682,
     "end_time": "2022-11-10T04:24:12.513643",
     "exception": false,
     "start_time": "2022-11-10T04:24:12.392961",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id='4_6'></a>\n",
    "## <p style=\"padding: 8px;color:white; display:fill;background-color:#aaaaaa; border-radius:5px; font-size:100%\"> <b>Gradient Boosting Classifier</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "id": "195e1bfe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:24:12.750085Z",
     "iopub.status.busy": "2022-11-10T04:24:12.749491Z",
     "iopub.status.idle": "2022-11-10T04:24:59.712410Z",
     "shell.execute_reply": "2022-11-10T04:24:59.710908Z"
    },
    "papermill": {
     "duration": 47.191606,
     "end_time": "2022-11-10T04:24:59.823864",
     "exception": false,
     "start_time": "2022-11-10T04:24:12.632258",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 28.4 s\n",
      "Wall time: 28.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "start = time.time()\n",
    "model = GradientBoostingClassifier().fit(X_train,y_train)\n",
    "end_train = time.time()\n",
    "y_predictions = model.predict(X_test) # These are the predictions from the test data.\n",
    "end_predict = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "id": "71a7ed06",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:25:00.035846Z",
     "iopub.status.busy": "2022-11-10T04:25:00.035185Z",
     "iopub.status.idle": "2022-11-10T04:25:00.077564Z",
     "shell.execute_reply": "2022-11-10T04:25:00.076284Z"
    },
    "papermill": {
     "duration": 0.152574,
     "end_time": "2022-11-10T04:25:00.079902",
     "exception": false,
     "start_time": "2022-11-10T04:24:59.927328",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 95.85%\n",
      "Recall: 95.85%\n",
      "Precision: 95.86%\n",
      "F1-Score: 95.85%\n",
      "time to train: 28.44 s\n",
      "time to predict: 0.02 s\n",
      "total: 28.46 s\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_predictions)\n",
    "recall = recall_score(y_test, y_predictions, average='weighted')\n",
    "precision = precision_score(y_test, y_predictions, average='weighted')\n",
    "f1s = f1_score(y_test, y_predictions, average='weighted')\n",
    "\n",
    "print(\"Accuracy: \"+ \"{:.2%}\".format(accuracy))\n",
    "print(\"Recall: \"+ \"{:.2%}\".format(recall))\n",
    "print(\"Precision: \"+ \"{:.2%}\".format(precision))\n",
    "print(\"F1-Score: \"+ \"{:.2%}\".format(f1s))\n",
    "print(\"time to train: \"+ \"{:.2f}\".format(end_train-start)+\" s\")\n",
    "print(\"time to predict: \"+\"{:.2f}\".format(end_predict-end_train)+\" s\")\n",
    "print(\"total: \"+\"{:.2f}\".format(end_predict-start)+\" s\")\n",
    "model_performance.loc['Gradient Boosting Classifier'] = [accuracy, recall, precision, f1s,end_train-start,end_predict-end_train,end_predict-start]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "id": "f8d3f3a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:25:00.299890Z",
     "iopub.status.busy": "2022-11-10T04:25:00.298994Z",
     "iopub.status.idle": "2022-11-10T04:25:00.639905Z",
     "shell.execute_reply": "2022-11-10T04:25:00.638892Z"
    },
    "papermill": {
     "duration": 0.454277,
     "end_time": "2022-11-10T04:25:00.642294",
     "exception": false,
     "start_time": "2022-11-10T04:25:00.188017",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 408,
       "width": 445
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=5,5 \n",
    "sns.set_style(\"white\")\n",
    "ConfusionMatrixDisplay.from_predictions(y_test,y_predictions, cmap=plt.cm.Blues) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "id": "b74e6dec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:25:00.858792Z",
     "iopub.status.busy": "2022-11-10T04:25:00.858367Z",
     "iopub.status.idle": "2022-11-10T04:25:01.327823Z",
     "shell.execute_reply": "2022-11-10T04:25:01.326870Z"
    },
    "papermill": {
     "duration": 0.582658,
     "end_time": "2022-11-10T04:25:01.330967",
     "exception": false,
     "start_time": "2022-11-10T04:25:00.748309",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 812,
       "width": 925
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=10,10\n",
    "sns.set_style(\"white\")\n",
    "feat_importances = pd.Series(model.feature_importances_, index=feature_names)\n",
    "feat_importances = feat_importances.groupby(level=0).mean()\n",
    "feat_importances.nlargest(20).plot(kind='barh').invert_yaxis()\n",
    "sns.despine()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11671edf",
   "metadata": {
    "papermill": {
     "duration": 0.108954,
     "end_time": "2022-11-10T04:25:01.550369",
     "exception": false,
     "start_time": "2022-11-10T04:25:01.441415",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id='4_7'></a>\n",
    "## <p style=\"padding: 8px;color:white; display:fill;background-color:#aaaaaa; border-radius:5px; font-size:100%\"> <b>Neural Network MLP</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "id": "263f94cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:25:01.770796Z",
     "iopub.status.busy": "2022-11-10T04:25:01.770493Z",
     "iopub.status.idle": "2022-11-10T04:25:45.725273Z",
     "shell.execute_reply": "2022-11-10T04:25:45.723700Z"
    },
    "papermill": {
     "duration": 44.26022,
     "end_time": "2022-11-10T04:25:45.919329",
     "exception": false,
     "start_time": "2022-11-10T04:25:01.659109",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 12 s\n",
      "Wall time: 12 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "start = time.time()\n",
    "model = MLPClassifier(hidden_layer_sizes = (20,20,), \n",
    "                      activation='relu', \n",
    "                      solver='adam',\n",
    "                      batch_size=2000,\n",
    "                      verbose=0).fit(X_train,y_train)\n",
    "end_train = time.time()\n",
    "y_predictions = model.predict(X_test) # These are the predictions from the test data.\n",
    "end_predict = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "id": "9a3e65e4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:25:46.164342Z",
     "iopub.status.busy": "2022-11-10T04:25:46.164001Z",
     "iopub.status.idle": "2022-11-10T04:25:46.208012Z",
     "shell.execute_reply": "2022-11-10T04:25:46.206731Z"
    },
    "papermill": {
     "duration": 0.170774,
     "end_time": "2022-11-10T04:25:46.210852",
     "exception": false,
     "start_time": "2022-11-10T04:25:46.040078",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 96.17%\n",
      "Recall: 96.17%\n",
      "Precision: 96.23%\n",
      "F1-Score: 96.17%\n",
      "time to train: 12.00 s\n",
      "time to predict: 0.01 s\n",
      "total: 12.01 s\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(y_test, y_predictions)\n",
    "recall = recall_score(y_test, y_predictions, average='weighted')\n",
    "precision = precision_score(y_test, y_predictions, average='weighted')\n",
    "f1s = f1_score(y_test, y_predictions, average='weighted')\n",
    "\n",
    "print(\"Accuracy: \"+ \"{:.2%}\".format(accuracy))\n",
    "print(\"Recall: \"+ \"{:.2%}\".format(recall))\n",
    "print(\"Precision: \"+ \"{:.2%}\".format(precision))\n",
    "print(\"F1-Score: \"+ \"{:.2%}\".format(f1s))\n",
    "print(\"time to train: \"+ \"{:.2f}\".format(end_train-start)+\" s\")\n",
    "print(\"time to predict: \"+\"{:.2f}\".format(end_predict-end_train)+\" s\")\n",
    "print(\"total: \"+\"{:.2f}\".format(end_predict-start)+\" s\")\n",
    "model_performance.loc['MLP'] = [accuracy, recall, precision, f1s,end_train-start,end_predict-end_train,end_predict-start]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "id": "d5b24c3a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:25:46.455344Z",
     "iopub.status.busy": "2022-11-10T04:25:46.455028Z",
     "iopub.status.idle": "2022-11-10T04:25:46.768628Z",
     "shell.execute_reply": "2022-11-10T04:25:46.767580Z"
    },
    "papermill": {
     "duration": 0.43109,
     "end_time": "2022-11-10T04:25:46.771172",
     "exception": false,
     "start_time": "2022-11-10T04:25:46.340082",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 408,
       "width": 445
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=5,5 \n",
    "sns.set_style(\"white\")\n",
    "ConfusionMatrixDisplay.from_predictions(y_test,y_predictions, cmap=plt.cm.Blues) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "id": "2ed41af1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:25:46.994010Z",
     "iopub.status.busy": "2022-11-10T04:25:46.993642Z",
     "iopub.status.idle": "2022-11-10T04:25:47.329362Z",
     "shell.execute_reply": "2022-11-10T04:25:47.328420Z"
    },
    "papermill": {
     "duration": 0.450292,
     "end_time": "2022-11-10T04:25:47.331855",
     "exception": false,
     "start_time": "2022-11-10T04:25:46.881563",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_a091a_row0_col0, #T_a091a_row0_col1, #T_a091a_row0_col2, #T_a091a_row0_col3, #T_a091a_row0_col5, #T_a091a_row0_col6, #T_a091a_row1_col4, #T_a091a_row2_col5 {\n",
       "  background-color: #3b4cc0;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a091a_row0_col4, #T_a091a_row5_col5 {\n",
       "  background-color: #4055c8;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_a091a_row1_col0, #T_a091a_row1_col1, #T_a091a_row1_col3 {\n",
       "  background-color: #d1dae9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a091a_row1_col2 {\n",
       "  background-color: #d2dbe8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_a091a_row1_col5, #T_a091a_row4_col0, #T_a091a_row4_col1, #T_a091a_row4_col2, #T_a091a_row4_col3, #T_a091a_row5_col4, #T_a091a_row5_col6 {\n",
       "  background-color: #b40426;\n",
       "  color: #f1f1f1;\n",
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       "<table id=\"T_a091a\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a091a_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_a091a_level0_col1\" class=\"col_heading level0 col1\" >Recall</th>\n",
       "      <th id=\"T_a091a_level0_col2\" class=\"col_heading level0 col2\" >Precision</th>\n",
       "      <th id=\"T_a091a_level0_col3\" class=\"col_heading level0 col3\" >F1-Score</th>\n",
       "      <th id=\"T_a091a_level0_col4\" class=\"col_heading level0 col4\" >time to train</th>\n",
       "      <th id=\"T_a091a_level0_col5\" class=\"col_heading level0 col5\" >time to predict</th>\n",
       "      <th id=\"T_a091a_level0_col6\" class=\"col_heading level0 col6\" >total time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a091a_level0_row0\" class=\"row_heading level0 row0\" >Logistic</th>\n",
       "      <td id=\"T_a091a_row0_col0\" class=\"data row0 col0\" >92.83%</td>\n",
       "      <td id=\"T_a091a_row0_col1\" class=\"data row0 col1\" >92.83%</td>\n",
       "      <td id=\"T_a091a_row0_col2\" class=\"data row0 col2\" >92.87%</td>\n",
       "      <td id=\"T_a091a_row0_col3\" class=\"data row0 col3\" >92.84%</td>\n",
       "      <td id=\"T_a091a_row0_col4\" class=\"data row0 col4\" >0.6</td>\n",
       "      <td id=\"T_a091a_row0_col5\" class=\"data row0 col5\" >0.0</td>\n",
       "      <td id=\"T_a091a_row0_col6\" class=\"data row0 col6\" >0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a091a_level0_row1\" class=\"row_heading level0 row1\" >kNN</th>\n",
       "      <td id=\"T_a091a_row1_col0\" class=\"data row1 col0\" >95.04%</td>\n",
       "      <td id=\"T_a091a_row1_col1\" class=\"data row1 col1\" >95.04%</td>\n",
       "      <td id=\"T_a091a_row1_col2\" class=\"data row1 col2\" >95.09%</td>\n",
       "      <td id=\"T_a091a_row1_col3\" class=\"data row1 col3\" >95.05%</td>\n",
       "      <td id=\"T_a091a_row1_col4\" class=\"data row1 col4\" >0.0</td>\n",
       "      <td id=\"T_a091a_row1_col5\" class=\"data row1 col5\" >1.1</td>\n",
       "      <td id=\"T_a091a_row1_col6\" class=\"data row1 col6\" >1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a091a_level0_row2\" class=\"row_heading level0 row2\" >Decision Tree</th>\n",
       "      <td id=\"T_a091a_row2_col0\" class=\"data row2 col0\" >96.45%</td>\n",
       "      <td id=\"T_a091a_row2_col1\" class=\"data row2 col1\" >96.45%</td>\n",
       "      <td id=\"T_a091a_row2_col2\" class=\"data row2 col2\" >96.45%</td>\n",
       "      <td id=\"T_a091a_row2_col3\" class=\"data row2 col3\" >96.45%</td>\n",
       "      <td id=\"T_a091a_row2_col4\" class=\"data row2 col4\" >0.9</td>\n",
       "      <td id=\"T_a091a_row2_col5\" class=\"data row2 col5\" >0.0</td>\n",
       "      <td id=\"T_a091a_row2_col6\" class=\"data row2 col6\" >0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a091a_level0_row3\" class=\"row_heading level0 row3\" >Extra Trees</th>\n",
       "      <td id=\"T_a091a_row3_col0\" class=\"data row3 col0\" >97.53%</td>\n",
       "      <td id=\"T_a091a_row3_col1\" class=\"data row3 col1\" >97.53%</td>\n",
       "      <td id=\"T_a091a_row3_col2\" class=\"data row3 col2\" >97.55%</td>\n",
       "      <td id=\"T_a091a_row3_col3\" class=\"data row3 col3\" >97.53%</td>\n",
       "      <td id=\"T_a091a_row3_col4\" class=\"data row3 col4\" >1.3</td>\n",
       "      <td id=\"T_a091a_row3_col5\" class=\"data row3 col5\" >0.1</td>\n",
       "      <td id=\"T_a091a_row3_col6\" class=\"data row3 col6\" >1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a091a_level0_row4\" class=\"row_heading level0 row4\" >Random Forest</th>\n",
       "      <td id=\"T_a091a_row4_col0\" class=\"data row4 col0\" >97.68%</td>\n",
       "      <td id=\"T_a091a_row4_col1\" class=\"data row4 col1\" >97.68%</td>\n",
       "      <td id=\"T_a091a_row4_col2\" class=\"data row4 col2\" >97.69%</td>\n",
       "      <td id=\"T_a091a_row4_col3\" class=\"data row4 col3\" >97.68%</td>\n",
       "      <td id=\"T_a091a_row4_col4\" class=\"data row4 col4\" >1.5</td>\n",
       "      <td id=\"T_a091a_row4_col5\" class=\"data row4 col5\" >0.0</td>\n",
       "      <td id=\"T_a091a_row4_col6\" class=\"data row4 col6\" >1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a091a_level0_row5\" class=\"row_heading level0 row5\" >Gradient Boosting Classifier</th>\n",
       "      <td id=\"T_a091a_row5_col0\" class=\"data row5 col0\" >95.85%</td>\n",
       "      <td id=\"T_a091a_row5_col1\" class=\"data row5 col1\" >95.85%</td>\n",
       "      <td id=\"T_a091a_row5_col2\" class=\"data row5 col2\" >95.86%</td>\n",
       "      <td id=\"T_a091a_row5_col3\" class=\"data row5 col3\" >95.85%</td>\n",
       "      <td id=\"T_a091a_row5_col4\" class=\"data row5 col4\" >28.4</td>\n",
       "      <td id=\"T_a091a_row5_col5\" class=\"data row5 col5\" >0.0</td>\n",
       "      <td id=\"T_a091a_row5_col6\" class=\"data row5 col6\" >28.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a091a_level0_row6\" class=\"row_heading level0 row6\" >MLP</th>\n",
       "      <td id=\"T_a091a_row6_col0\" class=\"data row6 col0\" >96.17%</td>\n",
       "      <td id=\"T_a091a_row6_col1\" class=\"data row6 col1\" >96.17%</td>\n",
       "      <td id=\"T_a091a_row6_col2\" class=\"data row6 col2\" >96.23%</td>\n",
       "      <td id=\"T_a091a_row6_col3\" class=\"data row6 col3\" >96.17%</td>\n",
       "      <td id=\"T_a091a_row6_col4\" class=\"data row6 col4\" >12.0</td>\n",
       "      <td id=\"T_a091a_row6_col5\" class=\"data row6 col5\" >0.0</td>\n",
       "      <td id=\"T_a091a_row6_col6\" class=\"data row6 col6\" >12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1aa906184d0>"
      ]
     },
     "execution_count": 510,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_performance.style.background_gradient(cmap='coolwarm').format({'Accuracy': '{:.2%}',\n",
    "                                                                     'Precision': '{:.2%}',\n",
    "                                                                     'Recall': '{:.2%}',\n",
    "                                                                     'F1-Score': '{:.2%}',\n",
    "                                                                     'time to train':'{:.1f}',\n",
    "                                                                     'time to predict':'{:.1f}',\n",
    "                                                                     'total time':'{:.1f}',\n",
    "                                                                     })"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12fa3021",
   "metadata": {
    "papermill": {
     "duration": 0.114709,
     "end_time": "2022-11-10T04:25:47.560671",
     "exception": false,
     "start_time": "2022-11-10T04:25:47.445962",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id='4_8'></a>\n",
    "## <p style=\"padding: 8px;color:white; display:fill;background-color:#aaaaaa; border-radius:5px; font-size:100%\"> <b>Neural Network MLP (Keras)</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "id": "c552f6df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install keras-metrics -i https://pypi.tuna.tsinghua.edu.cn/simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "id": "6bbe5851",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import libraries that will allow you to use keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM, GRU\n",
    "from keras import metrics\n",
    "# !pip install keras-metrics #It doesn't come with Google Colab\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "id": "97a2e1c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:26:09.172861Z",
     "iopub.status.busy": "2022-11-10T04:26:09.172506Z",
     "iopub.status.idle": "2022-11-10T04:26:09.181476Z",
     "shell.execute_reply": "2022-11-10T04:26:09.180373Z"
    },
    "papermill": {
     "duration": 0.125412,
     "end_time": "2022-11-10T04:26:09.183872",
     "exception": false,
     "start_time": "2022-11-10T04:26:09.058460",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from keras import backend as K\n",
    "\n",
    "def recall_m(y_true, y_pred):\n",
    "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "    possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
    "    recall = true_positives / (possible_positives + K.epsilon())\n",
    "    return recall\n",
    "\n",
    "def precision_m(y_true, y_pred):\n",
    "    true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "    predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
    "    precision = true_positives / (predicted_positives + K.epsilon())\n",
    "    return precision\n",
    "\n",
    "def f1_m(y_true, y_pred):\n",
    "    precision = precision_m(y_true, y_pred)\n",
    "    recall = recall_m(y_true, y_pred)\n",
    "    return 2*((precision*recall)/(precision+recall+K.epsilon()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "id": "55038644",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:26:09.415386Z",
     "iopub.status.busy": "2022-11-10T04:26:09.414600Z",
     "iopub.status.idle": "2022-11-10T04:26:50.849302Z",
     "shell.execute_reply": "2022-11-10T04:26:50.848046Z"
    },
    "papermill": {
     "duration": 41.555015,
     "end_time": "2022-11-10T04:26:50.852695",
     "exception": false,
     "start_time": "2022-11-10T04:26:09.297680",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "33/33 - 1s - loss: 2.1337 - accuracy: 0.4063 - f1_m: 1.6713e-04 - precision_m: 0.0470 - recall_m: 8.4146e-05 - 767ms/epoch - 23ms/step\n",
      "Epoch 2/200\n",
      "33/33 - 0s - loss: 0.8495 - accuracy: 0.7490 - f1_m: 0.5122 - precision_m: 0.6466 - recall_m: 0.5146 - 72ms/epoch - 2ms/step\n",
      "Epoch 3/200\n",
      "33/33 - 0s - loss: 0.4214 - accuracy: 0.7749 - f1_m: 0.7006 - precision_m: 0.5567 - recall_m: 0.9455 - 70ms/epoch - 2ms/step\n",
      "Epoch 4/200\n",
      "33/33 - 0s - loss: 0.3465 - accuracy: 0.8305 - f1_m: 0.7092 - precision_m: 0.5547 - recall_m: 0.9835 - 73ms/epoch - 2ms/step\n",
      "Epoch 5/200\n",
      "33/33 - 0s - loss: 0.3066 - accuracy: 0.8543 - f1_m: 0.7099 - precision_m: 0.5529 - recall_m: 0.9915 - 72ms/epoch - 2ms/step\n",
      "Epoch 6/200\n",
      "33/33 - 0s - loss: 0.2767 - accuracy: 0.8695 - f1_m: 0.7097 - precision_m: 0.5517 - recall_m: 0.9949 - 71ms/epoch - 2ms/step\n",
      "Epoch 7/200\n",
      "33/33 - 0s - loss: 0.2536 - accuracy: 0.8791 - f1_m: 0.7096 - precision_m: 0.5510 - recall_m: 0.9967 - 73ms/epoch - 2ms/step\n",
      "Epoch 8/200\n",
      "33/33 - 0s - loss: 0.2361 - accuracy: 0.8870 - f1_m: 0.7096 - precision_m: 0.5508 - recall_m: 0.9974 - 68ms/epoch - 2ms/step\n",
      "Epoch 9/200\n",
      "33/33 - 0s - loss: 0.2226 - accuracy: 0.8960 - f1_m: 0.7099 - precision_m: 0.5507 - recall_m: 0.9987 - 68ms/epoch - 2ms/step\n",
      "Epoch 10/200\n",
      "33/33 - 0s - loss: 0.2117 - accuracy: 0.9028 - f1_m: 0.7102 - precision_m: 0.5508 - recall_m: 0.9995 - 69ms/epoch - 2ms/step\n",
      "Epoch 11/200\n",
      "33/33 - 0s - loss: 0.2040 - accuracy: 0.9060 - f1_m: 0.7102 - precision_m: 0.5508 - recall_m: 0.9995 - 70ms/epoch - 2ms/step\n",
      "Epoch 12/200\n",
      "33/33 - 0s - loss: 0.1980 - accuracy: 0.9081 - f1_m: 0.7101 - precision_m: 0.5507 - recall_m: 0.9995 - 67ms/epoch - 2ms/step\n",
      "Epoch 13/200\n",
      "33/33 - 0s - loss: 0.1930 - accuracy: 0.9109 - f1_m: 0.7100 - precision_m: 0.5506 - recall_m: 0.9996 - 66ms/epoch - 2ms/step\n",
      "Epoch 14/200\n",
      "33/33 - 0s - loss: 0.1889 - accuracy: 0.9128 - f1_m: 0.7101 - precision_m: 0.5507 - recall_m: 0.9996 - 71ms/epoch - 2ms/step\n",
      "Epoch 15/200\n",
      "33/33 - 0s - loss: 0.1856 - accuracy: 0.9146 - f1_m: 0.7100 - precision_m: 0.5506 - recall_m: 0.9997 - 79ms/epoch - 2ms/step\n",
      "Epoch 16/200\n",
      "33/33 - 0s - loss: 0.1821 - accuracy: 0.9170 - f1_m: 0.7100 - precision_m: 0.5506 - recall_m: 0.9996 - 77ms/epoch - 2ms/step\n",
      "Epoch 17/200\n",
      "33/33 - 0s - loss: 0.1791 - accuracy: 0.9185 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9997 - 74ms/epoch - 2ms/step\n",
      "Epoch 18/200\n",
      "33/33 - 0s - loss: 0.1762 - accuracy: 0.9197 - f1_m: 0.7100 - precision_m: 0.5506 - recall_m: 0.9996 - 74ms/epoch - 2ms/step\n",
      "Epoch 19/200\n",
      "33/33 - 0s - loss: 0.1733 - accuracy: 0.9213 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9998 - 68ms/epoch - 2ms/step\n",
      "Epoch 20/200\n",
      "33/33 - 0s - loss: 0.1706 - accuracy: 0.9232 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9997 - 67ms/epoch - 2ms/step\n",
      "Epoch 21/200\n",
      "33/33 - 0s - loss: 0.1681 - accuracy: 0.9253 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9997 - 81ms/epoch - 2ms/step\n",
      "Epoch 22/200\n",
      "33/33 - 0s - loss: 0.1653 - accuracy: 0.9267 - f1_m: 0.7100 - precision_m: 0.5505 - recall_m: 0.9997 - 69ms/epoch - 2ms/step\n",
      "Epoch 23/200\n",
      "33/33 - 0s - loss: 0.1630 - accuracy: 0.9285 - f1_m: 0.7100 - precision_m: 0.5506 - recall_m: 0.9996 - 66ms/epoch - 2ms/step\n",
      "Epoch 24/200\n",
      "33/33 - 0s - loss: 0.1605 - accuracy: 0.9301 - f1_m: 0.7100 - precision_m: 0.5506 - recall_m: 0.9998 - 68ms/epoch - 2ms/step\n",
      "Epoch 25/200\n",
      "33/33 - 0s - loss: 0.1579 - accuracy: 0.9326 - f1_m: 0.7100 - precision_m: 0.5506 - recall_m: 0.9997 - 79ms/epoch - 2ms/step\n",
      "Epoch 26/200\n",
      "33/33 - 0s - loss: 0.1564 - accuracy: 0.9331 - f1_m: 0.7100 - precision_m: 0.5506 - recall_m: 0.9997 - 69ms/epoch - 2ms/step\n",
      "Epoch 27/200\n",
      "33/33 - 0s - loss: 0.1536 - accuracy: 0.9359 - f1_m: 0.7100 - precision_m: 0.5506 - recall_m: 0.9998 - 67ms/epoch - 2ms/step\n",
      "Epoch 28/200\n",
      "33/33 - 0s - loss: 0.1516 - accuracy: 0.9380 - f1_m: 0.7101 - precision_m: 0.5507 - recall_m: 0.9999 - 77ms/epoch - 2ms/step\n",
      "Epoch 29/200\n",
      "33/33 - 0s - loss: 0.1500 - accuracy: 0.9382 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9998 - 93ms/epoch - 3ms/step\n",
      "Epoch 30/200\n",
      "33/33 - 0s - loss: 0.1480 - accuracy: 0.9407 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 68ms/epoch - 2ms/step\n",
      "Epoch 31/200\n",
      "33/33 - 0s - loss: 0.1458 - accuracy: 0.9420 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 76ms/epoch - 2ms/step\n",
      "Epoch 32/200\n",
      "33/33 - 0s - loss: 0.1438 - accuracy: 0.9433 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 74ms/epoch - 2ms/step\n",
      "Epoch 33/200\n",
      "33/33 - 0s - loss: 0.1425 - accuracy: 0.9434 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 67ms/epoch - 2ms/step\n",
      "Epoch 34/200\n",
      "33/33 - 0s - loss: 0.1409 - accuracy: 0.9458 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 65ms/epoch - 2ms/step\n",
      "Epoch 35/200\n",
      "33/33 - 0s - loss: 0.1395 - accuracy: 0.9454 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 77ms/epoch - 2ms/step\n",
      "Epoch 36/200\n",
      "33/33 - 0s - loss: 0.1386 - accuracy: 0.9459 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 76ms/epoch - 2ms/step\n",
      "Epoch 37/200\n",
      "33/33 - 0s - loss: 0.1363 - accuracy: 0.9481 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 67ms/epoch - 2ms/step\n",
      "Epoch 38/200\n",
      "33/33 - 0s - loss: 0.1355 - accuracy: 0.9490 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 86ms/epoch - 3ms/step\n",
      "Epoch 39/200\n",
      "33/33 - 0s - loss: 0.1339 - accuracy: 0.9493 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 94ms/epoch - 3ms/step\n",
      "Epoch 40/200\n",
      "33/33 - 0s - loss: 0.1327 - accuracy: 0.9499 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 68ms/epoch - 2ms/step\n",
      "Epoch 41/200\n",
      "33/33 - 0s - loss: 0.1319 - accuracy: 0.9504 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 68ms/epoch - 2ms/step\n",
      "Epoch 42/200\n",
      "33/33 - 0s - loss: 0.1304 - accuracy: 0.9511 - f1_m: 0.7100 - precision_m: 0.5506 - recall_m: 0.9999 - 77ms/epoch - 2ms/step\n",
      "Epoch 43/200\n",
      "33/33 - 0s - loss: 0.1298 - accuracy: 0.9510 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 75ms/epoch - 2ms/step\n",
      "Epoch 44/200\n",
      "33/33 - 0s - loss: 0.1283 - accuracy: 0.9516 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 68ms/epoch - 2ms/step\n",
      "Epoch 45/200\n",
      "33/33 - 0s - loss: 0.1270 - accuracy: 0.9528 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 67ms/epoch - 2ms/step\n",
      "Epoch 46/200\n",
      "33/33 - 0s - loss: 0.1260 - accuracy: 0.9526 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 76ms/epoch - 2ms/step\n",
      "Epoch 47/200\n",
      "33/33 - 0s - loss: 0.1250 - accuracy: 0.9534 - f1_m: 0.7101 - precision_m: 0.5505 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 48/200\n",
      "33/33 - 0s - loss: 0.1256 - accuracy: 0.9529 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 65ms/epoch - 2ms/step\n",
      "Epoch 49/200\n",
      "33/33 - 0s - loss: 0.1233 - accuracy: 0.9543 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 67ms/epoch - 2ms/step\n",
      "Epoch 50/200\n",
      "33/33 - 0s - loss: 0.1223 - accuracy: 0.9540 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 78ms/epoch - 2ms/step\n",
      "Epoch 51/200\n",
      "33/33 - 0s - loss: 0.1216 - accuracy: 0.9550 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 88ms/epoch - 3ms/step\n",
      "Epoch 52/200\n",
      "33/33 - 0s - loss: 0.1208 - accuracy: 0.9552 - f1_m: 0.7100 - precision_m: 0.5506 - recall_m: 0.9999 - 67ms/epoch - 2ms/step\n",
      "Epoch 53/200\n",
      "33/33 - 0s - loss: 0.1204 - accuracy: 0.9548 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 73ms/epoch - 2ms/step\n",
      "Epoch 54/200\n",
      "33/33 - 0s - loss: 0.1190 - accuracy: 0.9559 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 74ms/epoch - 2ms/step\n",
      "Epoch 55/200\n",
      "33/33 - 0s - loss: 0.1193 - accuracy: 0.9550 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 78ms/epoch - 2ms/step\n",
      "Epoch 56/200\n",
      "33/33 - 0s - loss: 0.1179 - accuracy: 0.9560 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 67ms/epoch - 2ms/step\n",
      "Epoch 57/200\n",
      "33/33 - 0s - loss: 0.1168 - accuracy: 0.9567 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 73ms/epoch - 2ms/step\n",
      "Epoch 58/200\n",
      "33/33 - 0s - loss: 0.1161 - accuracy: 0.9567 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 59/200\n",
      "33/33 - 0s - loss: 0.1153 - accuracy: 0.9568 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 79ms/epoch - 2ms/step\n",
      "Epoch 60/200\n",
      "33/33 - 0s - loss: 0.1152 - accuracy: 0.9569 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 72ms/epoch - 2ms/step\n",
      "Epoch 61/200\n",
      "33/33 - 0s - loss: 0.1141 - accuracy: 0.9574 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 62/200\n",
      "33/33 - 0s - loss: 0.1135 - accuracy: 0.9576 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 73ms/epoch - 2ms/step\n",
      "Epoch 63/200\n",
      "33/33 - 0s - loss: 0.1130 - accuracy: 0.9579 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 72ms/epoch - 2ms/step\n",
      "Epoch 64/200\n",
      "33/33 - 0s - loss: 0.1121 - accuracy: 0.9579 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 65/200\n",
      "33/33 - 0s - loss: 0.1115 - accuracy: 0.9578 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 66/200\n",
      "33/33 - 0s - loss: 0.1106 - accuracy: 0.9589 - f1_m: 0.7100 - precision_m: 0.5505 - recall_m: 0.9999 - 70ms/epoch - 2ms/step\n",
      "Epoch 67/200\n",
      "33/33 - 0s - loss: 0.1103 - accuracy: 0.9586 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 70ms/epoch - 2ms/step\n",
      "Epoch 68/200\n",
      "33/33 - 0s - loss: 0.1101 - accuracy: 0.9584 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 69ms/epoch - 2ms/step\n",
      "Epoch 69/200\n",
      "33/33 - 0s - loss: 0.1091 - accuracy: 0.9587 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 69ms/epoch - 2ms/step\n",
      "Epoch 70/200\n",
      "33/33 - 0s - loss: 0.1083 - accuracy: 0.9591 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 70ms/epoch - 2ms/step\n",
      "Epoch 71/200\n",
      "33/33 - 0s - loss: 0.1076 - accuracy: 0.9593 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 72ms/epoch - 2ms/step\n",
      "Epoch 72/200\n",
      "33/33 - 0s - loss: 0.1075 - accuracy: 0.9593 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 65ms/epoch - 2ms/step\n",
      "Epoch 73/200\n",
      "33/33 - 0s - loss: 0.1069 - accuracy: 0.9595 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 64ms/epoch - 2ms/step\n",
      "Epoch 74/200\n",
      "33/33 - 0s - loss: 0.1071 - accuracy: 0.9593 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 66ms/epoch - 2ms/step\n",
      "Epoch 75/200\n",
      "33/33 - 0s - loss: 0.1061 - accuracy: 0.9597 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 76/200\n",
      "33/33 - 0s - loss: 0.1056 - accuracy: 0.9597 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 65ms/epoch - 2ms/step\n",
      "Epoch 77/200\n",
      "33/33 - 0s - loss: 0.1055 - accuracy: 0.9597 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 64ms/epoch - 2ms/step\n",
      "Epoch 78/200\n",
      "33/33 - 0s - loss: 0.1042 - accuracy: 0.9601 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 66ms/epoch - 2ms/step\n",
      "Epoch 79/200\n",
      "33/33 - 0s - loss: 0.1050 - accuracy: 0.9598 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 68ms/epoch - 2ms/step\n",
      "Epoch 80/200\n",
      "33/33 - 0s - loss: 0.1039 - accuracy: 0.9600 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 67ms/epoch - 2ms/step\n",
      "Epoch 81/200\n",
      "33/33 - 0s - loss: 0.1044 - accuracy: 0.9599 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 81ms/epoch - 2ms/step\n",
      "Epoch 82/200\n",
      "33/33 - 0s - loss: 0.1037 - accuracy: 0.9599 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 83ms/epoch - 3ms/step\n",
      "Epoch 83/200\n",
      "33/33 - 0s - loss: 0.1032 - accuracy: 0.9603 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 93ms/epoch - 3ms/step\n",
      "Epoch 84/200\n",
      "33/33 - 0s - loss: 0.1029 - accuracy: 0.9601 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 80ms/epoch - 2ms/step\n",
      "Epoch 85/200\n",
      "33/33 - 0s - loss: 0.1020 - accuracy: 0.9604 - f1_m: 0.7100 - precision_m: 0.5506 - recall_m: 1.0000 - 75ms/epoch - 2ms/step\n",
      "Epoch 86/200\n",
      "33/33 - 0s - loss: 0.1016 - accuracy: 0.9606 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 72ms/epoch - 2ms/step\n",
      "Epoch 87/200\n",
      "33/33 - 0s - loss: 0.1016 - accuracy: 0.9610 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 71ms/epoch - 2ms/step\n",
      "Epoch 88/200\n",
      "33/33 - 0s - loss: 0.1020 - accuracy: 0.9602 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 75ms/epoch - 2ms/step\n",
      "Epoch 89/200\n",
      "33/33 - 0s - loss: 0.1020 - accuracy: 0.9607 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 76ms/epoch - 2ms/step\n",
      "Epoch 90/200\n",
      "33/33 - 0s - loss: 0.1007 - accuracy: 0.9605 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 68ms/epoch - 2ms/step\n",
      "Epoch 91/200\n",
      "33/33 - 0s - loss: 0.1005 - accuracy: 0.9606 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 66ms/epoch - 2ms/step\n",
      "Epoch 92/200\n",
      "33/33 - 0s - loss: 0.1004 - accuracy: 0.9608 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 70ms/epoch - 2ms/step\n",
      "Epoch 93/200\n",
      "33/33 - 0s - loss: 0.0997 - accuracy: 0.9610 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 67ms/epoch - 2ms/step\n",
      "Epoch 94/200\n",
      "33/33 - 0s - loss: 0.1002 - accuracy: 0.9609 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 63ms/epoch - 2ms/step\n",
      "Epoch 95/200\n",
      "33/33 - 0s - loss: 0.0996 - accuracy: 0.9612 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 67ms/epoch - 2ms/step\n",
      "Epoch 96/200\n",
      "33/33 - 0s - loss: 0.1004 - accuracy: 0.9610 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 97/200\n",
      "33/33 - 0s - loss: 0.0994 - accuracy: 0.9618 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 75ms/epoch - 2ms/step\n",
      "Epoch 98/200\n",
      "33/33 - 0s - loss: 0.0986 - accuracy: 0.9619 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 73ms/epoch - 2ms/step\n",
      "Epoch 99/200\n",
      "33/33 - 0s - loss: 0.0990 - accuracy: 0.9615 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 100/200\n",
      "33/33 - 0s - loss: 0.0990 - accuracy: 0.9615 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 71ms/epoch - 2ms/step\n",
      "Epoch 101/200\n",
      "33/33 - 0s - loss: 0.0977 - accuracy: 0.9627 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 102/200\n",
      "33/33 - 0s - loss: 0.0974 - accuracy: 0.9622 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 78ms/epoch - 2ms/step\n",
      "Epoch 103/200\n",
      "33/33 - 0s - loss: 0.0975 - accuracy: 0.9620 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 70ms/epoch - 2ms/step\n",
      "Epoch 104/200\n",
      "33/33 - 0s - loss: 0.0969 - accuracy: 0.9625 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 69ms/epoch - 2ms/step\n",
      "Epoch 105/200\n",
      "33/33 - 0s - loss: 0.0969 - accuracy: 0.9624 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 72ms/epoch - 2ms/step\n",
      "Epoch 106/200\n",
      "33/33 - 0s - loss: 0.0965 - accuracy: 0.9629 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 72ms/epoch - 2ms/step\n",
      "Epoch 107/200\n",
      "33/33 - 0s - loss: 0.0967 - accuracy: 0.9625 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 68ms/epoch - 2ms/step\n",
      "Epoch 108/200\n",
      "33/33 - 0s - loss: 0.0960 - accuracy: 0.9627 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 0.9999 - 70ms/epoch - 2ms/step\n",
      "Epoch 109/200\n",
      "33/33 - 0s - loss: 0.0970 - accuracy: 0.9623 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 79ms/epoch - 2ms/step\n",
      "Epoch 110/200\n",
      "33/33 - 0s - loss: 0.0959 - accuracy: 0.9628 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 75ms/epoch - 2ms/step\n",
      "Epoch 111/200\n",
      "33/33 - 0s - loss: 0.0955 - accuracy: 0.9630 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 74ms/epoch - 2ms/step\n",
      "Epoch 112/200\n",
      "33/33 - 0s - loss: 0.0954 - accuracy: 0.9632 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 69ms/epoch - 2ms/step\n",
      "Epoch 113/200\n",
      "33/33 - 0s - loss: 0.0951 - accuracy: 0.9630 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 93ms/epoch - 3ms/step\n",
      "Epoch 114/200\n",
      "33/33 - 0s - loss: 0.0953 - accuracy: 0.9628 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 115/200\n",
      "33/33 - 0s - loss: 0.0959 - accuracy: 0.9625 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 116/200\n",
      "33/33 - 0s - loss: 0.0952 - accuracy: 0.9633 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 70ms/epoch - 2ms/step\n",
      "Epoch 117/200\n",
      "33/33 - 0s - loss: 0.0956 - accuracy: 0.9632 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 74ms/epoch - 2ms/step\n",
      "Epoch 118/200\n",
      "33/33 - 0s - loss: 0.0950 - accuracy: 0.9633 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 70ms/epoch - 2ms/step\n",
      "Epoch 119/200\n",
      "33/33 - 0s - loss: 0.0943 - accuracy: 0.9635 - f1_m: 0.7101 - precision_m: 0.5505 - recall_m: 1.0000 - 65ms/epoch - 2ms/step\n",
      "Epoch 120/200\n",
      "33/33 - 0s - loss: 0.0943 - accuracy: 0.9637 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 70ms/epoch - 2ms/step\n",
      "Epoch 121/200\n",
      "33/33 - 0s - loss: 0.0943 - accuracy: 0.9631 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 73ms/epoch - 2ms/step\n",
      "Epoch 122/200\n",
      "33/33 - 0s - loss: 0.0940 - accuracy: 0.9637 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 75ms/epoch - 2ms/step\n",
      "Epoch 123/200\n",
      "33/33 - 0s - loss: 0.0938 - accuracy: 0.9634 - f1_m: 0.7100 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 124/200\n",
      "33/33 - 0s - loss: 0.0935 - accuracy: 0.9638 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 69ms/epoch - 2ms/step\n",
      "Epoch 125/200\n",
      "33/33 - 0s - loss: 0.0942 - accuracy: 0.9635 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 67ms/epoch - 2ms/step\n",
      "Epoch 126/200\n",
      "33/33 - 0s - loss: 0.0941 - accuracy: 0.9635 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 67ms/epoch - 2ms/step\n",
      "Epoch 127/200\n",
      "33/33 - 0s - loss: 0.0934 - accuracy: 0.9635 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 128/200\n",
      "33/33 - 0s - loss: 0.0930 - accuracy: 0.9640 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 129/200\n",
      "33/33 - 0s - loss: 0.0927 - accuracy: 0.9641 - f1_m: 0.7102 - precision_m: 0.5506 - recall_m: 1.0000 - 68ms/epoch - 2ms/step\n",
      "Epoch 130/200\n",
      "33/33 - 0s - loss: 0.0936 - accuracy: 0.9631 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 131/200\n",
      "33/33 - 0s - loss: 0.0929 - accuracy: 0.9637 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 72ms/epoch - 2ms/step\n",
      "Epoch 132/200\n",
      "33/33 - 0s - loss: 0.0932 - accuracy: 0.9638 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 68ms/epoch - 2ms/step\n",
      "Epoch 133/200\n",
      "33/33 - 0s - loss: 0.0930 - accuracy: 0.9642 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 68ms/epoch - 2ms/step\n",
      "Epoch 134/200\n",
      "33/33 - 0s - loss: 0.0926 - accuracy: 0.9641 - f1_m: 0.7101 - precision_m: 0.5507 - recall_m: 1.0000 - 68ms/epoch - 2ms/step\n",
      "Epoch 135/200\n",
      "33/33 - 0s - loss: 0.0938 - accuracy: 0.9634 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 77ms/epoch - 2ms/step\n",
      "Epoch 136/200\n",
      "33/33 - 0s - loss: 0.0928 - accuracy: 0.9642 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 83ms/epoch - 3ms/step\n",
      "Epoch 137/200\n",
      "33/33 - 0s - loss: 0.0921 - accuracy: 0.9646 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 74ms/epoch - 2ms/step\n",
      "Epoch 138/200\n",
      "33/33 - 0s - loss: 0.0922 - accuracy: 0.9643 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 93ms/epoch - 3ms/step\n",
      "Epoch 139/200\n",
      "33/33 - 0s - loss: 0.0928 - accuracy: 0.9642 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 74ms/epoch - 2ms/step\n",
      "Epoch 140/200\n",
      "33/33 - 0s - loss: 0.0919 - accuracy: 0.9644 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 74ms/epoch - 2ms/step\n",
      "Epoch 141/200\n",
      "33/33 - 0s - loss: 0.0919 - accuracy: 0.9644 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 72ms/epoch - 2ms/step\n",
      "Epoch 142/200\n",
      "33/33 - 0s - loss: 0.0918 - accuracy: 0.9641 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 143/200\n",
      "33/33 - 0s - loss: 0.0916 - accuracy: 0.9645 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 68ms/epoch - 2ms/step\n",
      "Epoch 144/200\n",
      "33/33 - 0s - loss: 0.0921 - accuracy: 0.9645 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 68ms/epoch - 2ms/step\n",
      "Epoch 145/200\n",
      "33/33 - 0s - loss: 0.0919 - accuracy: 0.9642 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 69ms/epoch - 2ms/step\n",
      "Epoch 146/200\n",
      "33/33 - 0s - loss: 0.0911 - accuracy: 0.9649 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 68ms/epoch - 2ms/step\n",
      "Epoch 147/200\n",
      "33/33 - 0s - loss: 0.0914 - accuracy: 0.9644 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 68ms/epoch - 2ms/step\n",
      "Epoch 148/200\n",
      "33/33 - 0s - loss: 0.0912 - accuracy: 0.9646 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 67ms/epoch - 2ms/step\n",
      "Epoch 149/200\n",
      "33/33 - 0s - loss: 0.0913 - accuracy: 0.9648 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 150/200\n",
      "33/33 - 0s - loss: 0.0923 - accuracy: 0.9639 - f1_m: 0.7102 - precision_m: 0.5506 - recall_m: 1.0000 - 67ms/epoch - 2ms/step\n",
      "Epoch 151/200\n",
      "33/33 - 0s - loss: 0.0914 - accuracy: 0.9646 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 69ms/epoch - 2ms/step\n",
      "Epoch 152/200\n",
      "33/33 - 0s - loss: 0.0917 - accuracy: 0.9640 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 67ms/epoch - 2ms/step\n",
      "Epoch 153/200\n",
      "33/33 - 0s - loss: 0.0906 - accuracy: 0.9653 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 66ms/epoch - 2ms/step\n",
      "Epoch 154/200\n",
      "33/33 - 0s - loss: 0.0911 - accuracy: 0.9650 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 72ms/epoch - 2ms/step\n",
      "Epoch 155/200\n",
      "33/33 - 0s - loss: 0.0914 - accuracy: 0.9641 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 76ms/epoch - 2ms/step\n",
      "Epoch 156/200\n",
      "33/33 - 0s - loss: 0.0915 - accuracy: 0.9643 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 75ms/epoch - 2ms/step\n",
      "Epoch 157/200\n",
      "33/33 - 0s - loss: 0.0908 - accuracy: 0.9644 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 69ms/epoch - 2ms/step\n",
      "Epoch 158/200\n",
      "33/33 - 0s - loss: 0.0914 - accuracy: 0.9648 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 159/200\n",
      "33/33 - 0s - loss: 0.0911 - accuracy: 0.9643 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 160/200\n",
      "33/33 - 0s - loss: 0.0903 - accuracy: 0.9650 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 74ms/epoch - 2ms/step\n",
      "Epoch 161/200\n",
      "33/33 - 0s - loss: 0.0901 - accuracy: 0.9652 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 162/200\n",
      "33/33 - 0s - loss: 0.0905 - accuracy: 0.9648 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 163/200\n",
      "33/33 - 0s - loss: 0.0903 - accuracy: 0.9649 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 72ms/epoch - 2ms/step\n",
      "Epoch 164/200\n",
      "33/33 - 0s - loss: 0.0905 - accuracy: 0.9650 - f1_m: 0.7102 - precision_m: 0.5507 - recall_m: 1.0000 - 72ms/epoch - 2ms/step\n",
      "Epoch 165/200\n",
      "33/33 - 0s - loss: 0.0902 - accuracy: 0.9649 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 70ms/epoch - 2ms/step\n",
      "Epoch 166/200\n",
      "33/33 - 0s - loss: 0.0900 - accuracy: 0.9654 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 69ms/epoch - 2ms/step\n",
      "Epoch 167/200\n",
      "33/33 - 0s - loss: 0.0906 - accuracy: 0.9645 - f1_m: 0.7101 - precision_m: 0.5507 - recall_m: 1.0000 - 69ms/epoch - 2ms/step\n",
      "Epoch 168/200\n",
      "33/33 - 0s - loss: 0.0904 - accuracy: 0.9650 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 169/200\n",
      "33/33 - 0s - loss: 0.0898 - accuracy: 0.9651 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 101ms/epoch - 3ms/step\n",
      "Epoch 170/200\n",
      "33/33 - 0s - loss: 0.0897 - accuracy: 0.9652 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 101ms/epoch - 3ms/step\n",
      "Epoch 171/200\n",
      "33/33 - 0s - loss: 0.0899 - accuracy: 0.9653 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 117ms/epoch - 4ms/step\n",
      "Epoch 172/200\n",
      "33/33 - 0s - loss: 0.0901 - accuracy: 0.9650 - f1_m: 0.7102 - precision_m: 0.5506 - recall_m: 1.0000 - 90ms/epoch - 3ms/step\n",
      "Epoch 173/200\n",
      "33/33 - 0s - loss: 0.0900 - accuracy: 0.9653 - f1_m: 0.7102 - precision_m: 0.5506 - recall_m: 1.0000 - 78ms/epoch - 2ms/step\n",
      "Epoch 174/200\n",
      "33/33 - 0s - loss: 0.0894 - accuracy: 0.9651 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 75ms/epoch - 2ms/step\n",
      "Epoch 175/200\n",
      "33/33 - 0s - loss: 0.0893 - accuracy: 0.9656 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 176/200\n",
      "33/33 - 0s - loss: 0.0894 - accuracy: 0.9653 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 91ms/epoch - 3ms/step\n",
      "Epoch 177/200\n",
      "33/33 - 0s - loss: 0.0900 - accuracy: 0.9651 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 73ms/epoch - 2ms/step\n",
      "Epoch 178/200\n",
      "33/33 - 0s - loss: 0.0897 - accuracy: 0.9649 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 76ms/epoch - 2ms/step\n",
      "Epoch 179/200\n",
      "33/33 - 0s - loss: 0.0921 - accuracy: 0.9639 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 81ms/epoch - 2ms/step\n",
      "Epoch 180/200\n",
      "33/33 - 0s - loss: 0.0902 - accuracy: 0.9651 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 85ms/epoch - 3ms/step\n",
      "Epoch 181/200\n",
      "33/33 - 0s - loss: 0.0894 - accuracy: 0.9650 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 78ms/epoch - 2ms/step\n",
      "Epoch 182/200\n",
      "33/33 - 0s - loss: 0.0895 - accuracy: 0.9652 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 76ms/epoch - 2ms/step\n",
      "Epoch 183/200\n",
      "33/33 - 0s - loss: 0.0892 - accuracy: 0.9654 - f1_m: 0.7101 - precision_m: 0.5507 - recall_m: 1.0000 - 75ms/epoch - 2ms/step\n",
      "Epoch 184/200\n",
      "33/33 - 0s - loss: 0.0890 - accuracy: 0.9654 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 79ms/epoch - 2ms/step\n",
      "Epoch 185/200\n",
      "33/33 - 0s - loss: 0.0891 - accuracy: 0.9657 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 75ms/epoch - 2ms/step\n",
      "Epoch 186/200\n",
      "33/33 - 0s - loss: 0.0892 - accuracy: 0.9655 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 76ms/epoch - 2ms/step\n",
      "Epoch 187/200\n",
      "33/33 - 0s - loss: 0.0894 - accuracy: 0.9650 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 74ms/epoch - 2ms/step\n",
      "Epoch 188/200\n",
      "33/33 - 0s - loss: 0.0887 - accuracy: 0.9657 - f1_m: 0.7100 - precision_m: 0.5506 - recall_m: 1.0000 - 74ms/epoch - 2ms/step\n",
      "Epoch 189/200\n",
      "33/33 - 0s - loss: 0.0891 - accuracy: 0.9650 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 70ms/epoch - 2ms/step\n",
      "Epoch 190/200\n",
      "33/33 - 0s - loss: 0.0892 - accuracy: 0.9652 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 75ms/epoch - 2ms/step\n",
      "Epoch 191/200\n",
      "33/33 - 0s - loss: 0.0898 - accuracy: 0.9651 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 75ms/epoch - 2ms/step\n",
      "Epoch 192/200\n",
      "33/33 - 0s - loss: 0.0889 - accuracy: 0.9651 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 75ms/epoch - 2ms/step\n",
      "Epoch 193/200\n",
      "33/33 - 0s - loss: 0.0892 - accuracy: 0.9652 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 76ms/epoch - 2ms/step\n",
      "Epoch 194/200\n",
      "33/33 - 0s - loss: 0.0892 - accuracy: 0.9647 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 73ms/epoch - 2ms/step\n",
      "Epoch 195/200\n",
      "33/33 - 0s - loss: 0.0883 - accuracy: 0.9659 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 77ms/epoch - 2ms/step\n",
      "Epoch 196/200\n",
      "33/33 - 0s - loss: 0.0886 - accuracy: 0.9655 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 71ms/epoch - 2ms/step\n",
      "Epoch 197/200\n",
      "33/33 - 0s - loss: 0.0884 - accuracy: 0.9658 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 73ms/epoch - 2ms/step\n",
      "Epoch 198/200\n",
      "33/33 - 0s - loss: 0.0888 - accuracy: 0.9654 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 72ms/epoch - 2ms/step\n",
      "Epoch 199/200\n",
      "33/33 - 0s - loss: 0.0883 - accuracy: 0.9653 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 74ms/epoch - 2ms/step\n",
      "Epoch 200/200\n",
      "33/33 - 0s - loss: 0.0884 - accuracy: 0.9652 - f1_m: 0.7101 - precision_m: 0.5506 - recall_m: 1.0000 - 72ms/epoch - 2ms/step\n"
     ]
    }
   ],
   "source": [
    "#Build the feed forward neural network model\n",
    "def build_model():\n",
    "    model = Sequential()\n",
    "    model.add(Dense(20, input_dim=56, activation='relu'))\n",
    "    model.add(Dense(20, activation='relu'))\n",
    "    model.add(Dense(20, activation='softmax')) #for multiclass classification\n",
    "    #Compile the model\n",
    "    model.compile(loss='sparse_categorical_crossentropy', optimizer='adam',\n",
    "                  metrics=['accuracy',f1_m,precision_m, recall_m]\n",
    "                 )\n",
    "    return model\n",
    "\n",
    "#institate the model\n",
    "model = build_model()\n",
    "\n",
    "#fit the model\n",
    "start = time.time()\n",
    "model.fit(X_train, y_train, epochs=200, batch_size=2000,verbose=2)\n",
    "end_train = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "id": "ce4e801c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:26:51.369569Z",
     "iopub.status.busy": "2022-11-10T04:26:51.368667Z",
     "iopub.status.idle": "2022-11-10T04:26:52.589792Z",
     "shell.execute_reply": "2022-11-10T04:26:52.588747Z"
    },
    "papermill": {
     "duration": 1.479945,
     "end_time": "2022-11-10T04:26:52.592437",
     "exception": false,
     "start_time": "2022-11-10T04:26:51.112492",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "515/515 [==============================] - 1s 990us/step - loss: 0.0922 - accuracy: 0.9622 - f1_m: 0.7060 - precision_m: 0.5506 - recall_m: 1.0000\n"
     ]
    }
   ],
   "source": [
    "#Evaluate the neural network\n",
    "loss, accuracy, f1s, precision, recall = model.evaluate(X_test, y_test)\n",
    "end_predict = time.time()\n",
    "model_performance.loc['MLP (Keras)'] = [accuracy, accuracy, accuracy, accuracy,end_train-start,end_predict-end_train,end_predict-start]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e59a9b4",
   "metadata": {
    "papermill": {
     "duration": 0.245186,
     "end_time": "2022-11-10T04:26:53.085467",
     "exception": false,
     "start_time": "2022-11-10T04:26:52.840281",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id='4_9'></a>\n",
    "## <p style=\"padding: 8px;color:white; display:fill;background-color:#aaaaaa; border-radius:5px; font-size:100%\"> <b>GRU (Keras)</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "id": "4dd3600c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:26:53.592679Z",
     "iopub.status.busy": "2022-11-10T04:26:53.592332Z",
     "iopub.status.idle": "2022-11-10T04:28:16.650738Z",
     "shell.execute_reply": "2022-11-10T04:28:16.649821Z"
    },
    "papermill": {
     "duration": 83.307793,
     "end_time": "2022-11-10T04:28:16.653282",
     "exception": false,
     "start_time": "2022-11-10T04:26:53.345489",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65865\n",
      "Epoch 1/200\n",
      "33/33 - 3s - loss: 2.0235 - accuracy: 0.5752 - 3s/epoch - 98ms/step\n",
      "Epoch 2/200\n",
      "33/33 - 0s - loss: 1.3118 - accuracy: 0.7051 - 171ms/epoch - 5ms/step\n",
      "Epoch 3/200\n",
      "33/33 - 0s - loss: 0.7384 - accuracy: 0.7500 - 168ms/epoch - 5ms/step\n",
      "Epoch 4/200\n",
      "33/33 - 0s - loss: 0.5348 - accuracy: 0.7775 - 198ms/epoch - 6ms/step\n",
      "Epoch 5/200\n",
      "33/33 - 0s - loss: 0.4478 - accuracy: 0.7885 - 219ms/epoch - 7ms/step\n",
      "Epoch 6/200\n",
      "33/33 - 0s - loss: 0.3794 - accuracy: 0.8161 - 185ms/epoch - 6ms/step\n",
      "Epoch 7/200\n",
      "33/33 - 0s - loss: 0.3216 - accuracy: 0.8618 - 167ms/epoch - 5ms/step\n",
      "Epoch 8/200\n",
      "33/33 - 0s - loss: 0.2781 - accuracy: 0.8787 - 180ms/epoch - 5ms/step\n",
      "Epoch 9/200\n",
      "33/33 - 0s - loss: 0.2489 - accuracy: 0.8922 - 157ms/epoch - 5ms/step\n",
      "Epoch 10/200\n",
      "33/33 - 0s - loss: 0.2293 - accuracy: 0.9009 - 164ms/epoch - 5ms/step\n",
      "Epoch 11/200\n",
      "33/33 - 0s - loss: 0.2151 - accuracy: 0.9077 - 154ms/epoch - 5ms/step\n",
      "Epoch 12/200\n",
      "33/33 - 0s - loss: 0.2041 - accuracy: 0.9117 - 154ms/epoch - 5ms/step\n",
      "Epoch 13/200\n",
      "33/33 - 0s - loss: 0.1947 - accuracy: 0.9168 - 160ms/epoch - 5ms/step\n",
      "Epoch 14/200\n",
      "33/33 - 0s - loss: 0.1865 - accuracy: 0.9209 - 176ms/epoch - 5ms/step\n",
      "Epoch 15/200\n",
      "33/33 - 0s - loss: 0.1790 - accuracy: 0.9253 - 182ms/epoch - 6ms/step\n",
      "Epoch 16/200\n",
      "33/33 - 0s - loss: 0.1721 - accuracy: 0.9294 - 209ms/epoch - 6ms/step\n",
      "Epoch 17/200\n",
      "33/33 - 0s - loss: 0.1661 - accuracy: 0.9327 - 180ms/epoch - 5ms/step\n",
      "Epoch 18/200\n",
      "33/33 - 0s - loss: 0.1607 - accuracy: 0.9355 - 164ms/epoch - 5ms/step\n",
      "Epoch 19/200\n",
      "33/33 - 0s - loss: 0.1560 - accuracy: 0.9379 - 173ms/epoch - 5ms/step\n",
      "Epoch 20/200\n",
      "33/33 - 0s - loss: 0.1518 - accuracy: 0.9404 - 176ms/epoch - 5ms/step\n",
      "Epoch 21/200\n",
      "33/33 - 0s - loss: 0.1483 - accuracy: 0.9418 - 174ms/epoch - 5ms/step\n",
      "Epoch 22/200\n",
      "33/33 - 0s - loss: 0.1449 - accuracy: 0.9433 - 172ms/epoch - 5ms/step\n",
      "Epoch 23/200\n",
      "33/33 - 0s - loss: 0.1422 - accuracy: 0.9451 - 160ms/epoch - 5ms/step\n",
      "Epoch 24/200\n",
      "33/33 - 0s - loss: 0.1394 - accuracy: 0.9462 - 197ms/epoch - 6ms/step\n",
      "Epoch 25/200\n",
      "33/33 - 0s - loss: 0.1372 - accuracy: 0.9473 - 184ms/epoch - 6ms/step\n",
      "Epoch 26/200\n",
      "33/33 - 0s - loss: 0.1351 - accuracy: 0.9489 - 166ms/epoch - 5ms/step\n",
      "Epoch 27/200\n",
      "33/33 - 0s - loss: 0.1337 - accuracy: 0.9494 - 161ms/epoch - 5ms/step\n",
      "Epoch 28/200\n",
      "33/33 - 0s - loss: 0.1318 - accuracy: 0.9502 - 163ms/epoch - 5ms/step\n",
      "Epoch 29/200\n",
      "33/33 - 0s - loss: 0.1298 - accuracy: 0.9514 - 173ms/epoch - 5ms/step\n",
      "Epoch 30/200\n",
      "33/33 - 0s - loss: 0.1289 - accuracy: 0.9509 - 190ms/epoch - 6ms/step\n",
      "Epoch 31/200\n",
      "33/33 - 0s - loss: 0.1275 - accuracy: 0.9524 - 181ms/epoch - 5ms/step\n",
      "Epoch 32/200\n",
      "33/33 - 0s - loss: 0.1258 - accuracy: 0.9534 - 170ms/epoch - 5ms/step\n",
      "Epoch 33/200\n",
      "33/33 - 0s - loss: 0.1245 - accuracy: 0.9540 - 160ms/epoch - 5ms/step\n",
      "Epoch 34/200\n",
      "33/33 - 0s - loss: 0.1236 - accuracy: 0.9545 - 166ms/epoch - 5ms/step\n",
      "Epoch 35/200\n",
      "33/33 - 0s - loss: 0.1232 - accuracy: 0.9541 - 215ms/epoch - 7ms/step\n",
      "Epoch 36/200\n",
      "33/33 - 0s - loss: 0.1216 - accuracy: 0.9551 - 183ms/epoch - 6ms/step\n",
      "Epoch 37/200\n",
      "33/33 - 0s - loss: 0.1205 - accuracy: 0.9553 - 167ms/epoch - 5ms/step\n",
      "Epoch 38/200\n",
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      "Epoch 71/200\n",
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      "Epoch 75/200\n",
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      "Epoch 76/200\n",
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      "Epoch 77/200\n",
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      "Epoch 78/200\n",
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      "Epoch 79/200\n",
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      "Epoch 80/200\n",
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      "Epoch 81/200\n",
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      "Epoch 82/200\n",
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      "Epoch 83/200\n",
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      "Epoch 84/200\n",
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      "Epoch 85/200\n",
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      "Epoch 86/200\n",
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      "Epoch 87/200\n",
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      "Epoch 88/200\n",
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      "Epoch 89/200\n",
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      "Epoch 90/200\n",
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      "Epoch 91/200\n",
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      "Epoch 92/200\n",
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      "Epoch 93/200\n",
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      "Epoch 94/200\n",
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      "Epoch 95/200\n",
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      "Epoch 96/200\n",
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      "Epoch 97/200\n",
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      "Epoch 98/200\n",
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      "Epoch 99/200\n",
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      "Epoch 100/200\n",
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      "Epoch 101/200\n",
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      "Epoch 102/200\n",
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      "Epoch 105/200\n",
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      "Epoch 106/200\n",
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      "Epoch 107/200\n",
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      "Epoch 108/200\n",
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      "Epoch 109/200\n",
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      "Epoch 110/200\n",
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      "Epoch 111/200\n",
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      "Epoch 112/200\n",
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      "Epoch 113/200\n",
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      "Epoch 114/200\n",
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      "Epoch 115/200\n",
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      "Epoch 117/200\n",
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      "Epoch 118/200\n",
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      "Epoch 119/200\n",
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      "Epoch 120/200\n",
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      "Epoch 121/200\n",
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      "Epoch 122/200\n",
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      "Epoch 123/200\n",
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      "Epoch 124/200\n",
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      "Epoch 125/200\n",
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      "Epoch 126/200\n",
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      "Epoch 127/200\n",
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      "Epoch 129/200\n",
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      "Epoch 130/200\n",
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      "Epoch 131/200\n",
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      "Epoch 132/200\n",
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      "Epoch 133/200\n",
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      "Epoch 134/200\n",
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      "Epoch 136/200\n",
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      "Epoch 137/200\n",
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      "Epoch 138/200\n",
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      "Epoch 140/200\n",
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      "Epoch 141/200\n",
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      "Epoch 142/200\n",
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      "Epoch 143/200\n",
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      "Epoch 144/200\n",
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      "Epoch 145/200\n",
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      "Epoch 146/200\n",
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      "Epoch 147/200\n",
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      "Epoch 148/200\n",
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      "Epoch 154/200\n",
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      "Epoch 155/200\n",
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      "Epoch 156/200\n",
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      "Epoch 158/200\n",
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      "Epoch 159/200\n",
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      "Epoch 160/200\n",
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      "Epoch 161/200\n",
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      "Epoch 162/200\n",
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      "Epoch 163/200\n",
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      "Epoch 164/200\n",
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      "Epoch 165/200\n",
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      "Epoch 166/200\n",
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      "Epoch 167/200\n",
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      "Epoch 168/200\n",
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      "Epoch 169/200\n",
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      "Epoch 171/200\n",
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      "Epoch 172/200\n",
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      "Epoch 173/200\n",
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      "Epoch 174/200\n",
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      "Epoch 175/200\n",
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      "Epoch 176/200\n",
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      "Epoch 177/200\n",
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      "Epoch 178/200\n",
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      "Epoch 179/200\n",
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      "Epoch 180/200\n",
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      "Epoch 181/200\n",
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      "Epoch 182/200\n",
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      "Epoch 183/200\n",
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      "Epoch 184/200\n",
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      "Epoch 185/200\n",
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      "Epoch 186/200\n",
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      "Epoch 187/200\n",
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      "Epoch 188/200\n",
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      "Epoch 189/200\n",
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      "Epoch 190/200\n",
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      "Epoch 191/200\n",
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      "Epoch 192/200\n",
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      "Epoch 193/200\n",
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      "Epoch 194/200\n",
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      "Epoch 195/200\n",
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      "Epoch 196/200\n",
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      "Epoch 197/200\n",
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      "Epoch 198/200\n",
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      "Epoch 199/200\n",
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      "Epoch 200/200\n",
      "33/33 - 0s - loss: 0.0822 - accuracy: 0.9684 - 155ms/epoch - 5ms/step\n"
     ]
    }
   ],
   "source": [
    "#Build the neural network model\n",
    "def build_model():\n",
    "    model = Sequential()\n",
    "    model.add(GRU(20, return_sequences=True,input_shape=(1,56)))\n",
    "    model.add(GRU(20, return_sequences=True))\n",
    "    model.add(Dense(10, activation='softmax')) #for multiclass classification\n",
    "    #Compile the model\n",
    "    model.compile(loss='sparse_categorical_crossentropy', optimizer='adam',\n",
    "                  # metrics=['accuracy',f1_m,precision_m, recall_m]\n",
    "                  metrics=['accuracy']\n",
    "                 )\n",
    "    return model\n",
    "\n",
    "#The GRU input layer must be 3D.\n",
    "#The meaning of the 3 input dimensions are: samples, time steps, and features.\n",
    "#reshape input data\n",
    "X_train_array = np.array(X_train) #array has been declared in the previous cell\n",
    "print(len(X_train_array))\n",
    "X_train_reshaped = X_train_array.reshape(X_train_array.shape[0],1,56)\n",
    "\n",
    "#reshape output data\n",
    "X_test_array=  np.array(X_test)\n",
    "X_test_reshaped = X_test_array.reshape(X_test_array.shape[0],1,56) \n",
    "\n",
    "\n",
    "#institate the model\n",
    "model = build_model()\n",
    "\n",
    "start = time.time()\n",
    "#fit the model\n",
    "model.fit(X_train_reshaped, y_train, epochs=200, batch_size=2000,verbose=2)\n",
    "end_train = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "id": "1d0b842d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:28:17.423452Z",
     "iopub.status.busy": "2022-11-10T04:28:17.422682Z",
     "iopub.status.idle": "2022-11-10T04:28:20.801112Z",
     "shell.execute_reply": "2022-11-10T04:28:20.800174Z"
    },
    "papermill": {
     "duration": 3.766122,
     "end_time": "2022-11-10T04:28:20.803637",
     "exception": false,
     "start_time": "2022-11-10T04:28:17.037515",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "515/515 [==============================] - 1s 1ms/step - loss: 0.0870 - accuracy: 0.9658\n"
     ]
    }
   ],
   "source": [
    "loss, accuracy = model.evaluate(X_test_reshaped, y_test)\n",
    "# loss, accuracy, f1s, precision, recall = model.evaluate(X_test_reshaped, y_test)\n",
    "end_predict = time.time()\n",
    "model_performance.loc['GRU (Keras)'] = [accuracy, accuracy, accuracy, accuracy, end_train-start,end_predict-end_train,end_predict-start]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 518,
   "id": "ce76e7f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:28:21.609040Z",
     "iopub.status.busy": "2022-11-10T04:28:21.608390Z",
     "iopub.status.idle": "2022-11-10T04:28:21.614631Z",
     "shell.execute_reply": "2022-11-10T04:28:21.614018Z"
    },
    "papermill": {
     "duration": 0.412783,
     "end_time": "2022-11-10T04:28:21.616761",
     "exception": false,
     "start_time": "2022-11-10T04:28:21.203978",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(82332, 56)"
      ]
     },
     "execution_count": 518,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87c9b20a",
   "metadata": {
    "papermill": {
     "duration": 0.39046,
     "end_time": "2022-11-10T04:28:22.399241",
     "exception": false,
     "start_time": "2022-11-10T04:28:22.008781",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id='4_10'></a>\n",
    "## <p style=\"padding: 8px;color:white; display:fill;background-color:#aaaaaa; border-radius:5px; font-size:100%\"> <b>LSTM (Keras)</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "id": "6b062d46",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:28:23.194724Z",
     "iopub.status.busy": "2022-11-10T04:28:23.194413Z",
     "iopub.status.idle": "2022-11-10T04:29:49.773300Z",
     "shell.execute_reply": "2022-11-10T04:29:49.772202Z"
    },
    "papermill": {
     "duration": 86.973768,
     "end_time": "2022-11-10T04:29:49.776419",
     "exception": false,
     "start_time": "2022-11-10T04:28:22.802651",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65865\n",
      "Epoch 1/200\n",
      "33/33 - 3s - loss: 2.2091 - accuracy: 0.6175 - 3s/epoch - 92ms/step\n",
      "Epoch 2/200\n",
      "33/33 - 0s - loss: 1.8516 - accuracy: 0.7437 - 169ms/epoch - 5ms/step\n",
      "Epoch 3/200\n",
      "33/33 - 0s - loss: 1.2657 - accuracy: 0.7470 - 165ms/epoch - 5ms/step\n",
      "Epoch 4/200\n",
      "33/33 - 0s - loss: 0.8374 - accuracy: 0.7483 - 164ms/epoch - 5ms/step\n",
      "Epoch 5/200\n",
      "33/33 - 0s - loss: 0.6515 - accuracy: 0.7484 - 163ms/epoch - 5ms/step\n",
      "Epoch 6/200\n",
      "33/33 - 0s - loss: 0.5720 - accuracy: 0.7483 - 187ms/epoch - 6ms/step\n",
      "Epoch 7/200\n",
      "33/33 - 0s - loss: 0.5138 - accuracy: 0.7590 - 178ms/epoch - 5ms/step\n",
      "Epoch 8/200\n",
      "33/33 - 0s - loss: 0.4523 - accuracy: 0.7773 - 176ms/epoch - 5ms/step\n",
      "Epoch 9/200\n",
      "33/33 - 0s - loss: 0.3911 - accuracy: 0.8404 - 165ms/epoch - 5ms/step\n",
      "Epoch 10/200\n",
      "33/33 - 0s - loss: 0.3354 - accuracy: 0.8687 - 165ms/epoch - 5ms/step\n",
      "Epoch 11/200\n",
      "33/33 - 0s - loss: 0.2913 - accuracy: 0.8813 - 168ms/epoch - 5ms/step\n",
      "Epoch 12/200\n",
      "33/33 - 0s - loss: 0.2604 - accuracy: 0.8952 - 170ms/epoch - 5ms/step\n",
      "Epoch 13/200\n",
      "33/33 - 0s - loss: 0.2392 - accuracy: 0.9020 - 183ms/epoch - 6ms/step\n",
      "Epoch 14/200\n",
      "33/33 - 0s - loss: 0.2239 - accuracy: 0.9086 - 174ms/epoch - 5ms/step\n",
      "Epoch 15/200\n",
      "33/33 - 0s - loss: 0.2121 - accuracy: 0.9131 - 176ms/epoch - 5ms/step\n",
      "Epoch 16/200\n",
      "33/33 - 0s - loss: 0.2027 - accuracy: 0.9150 - 166ms/epoch - 5ms/step\n",
      "Epoch 17/200\n",
      "33/33 - 0s - loss: 0.1939 - accuracy: 0.9196 - 165ms/epoch - 5ms/step\n",
      "Epoch 18/200\n",
      "33/33 - 0s - loss: 0.1865 - accuracy: 0.9234 - 167ms/epoch - 5ms/step\n",
      "Epoch 19/200\n",
      "33/33 - 0s - loss: 0.1798 - accuracy: 0.9266 - 171ms/epoch - 5ms/step\n",
      "Epoch 20/200\n",
      "33/33 - 0s - loss: 0.1740 - accuracy: 0.9293 - 172ms/epoch - 5ms/step\n",
      "Epoch 21/200\n",
      "33/33 - 0s - loss: 0.1681 - accuracy: 0.9323 - 164ms/epoch - 5ms/step\n",
      "Epoch 22/200\n",
      "33/33 - 0s - loss: 0.1626 - accuracy: 0.9352 - 165ms/epoch - 5ms/step\n",
      "Epoch 23/200\n",
      "33/33 - 0s - loss: 0.1580 - accuracy: 0.9375 - 166ms/epoch - 5ms/step\n",
      "Epoch 24/200\n",
      "33/33 - 0s - loss: 0.1536 - accuracy: 0.9391 - 172ms/epoch - 5ms/step\n",
      "Epoch 25/200\n",
      "33/33 - 0s - loss: 0.1494 - accuracy: 0.9414 - 171ms/epoch - 5ms/step\n",
      "Epoch 26/200\n",
      "33/33 - 0s - loss: 0.1458 - accuracy: 0.9443 - 172ms/epoch - 5ms/step\n",
      "Epoch 27/200\n",
      "33/33 - 0s - loss: 0.1427 - accuracy: 0.9455 - 171ms/epoch - 5ms/step\n",
      "Epoch 28/200\n",
      "33/33 - 0s - loss: 0.1396 - accuracy: 0.9467 - 168ms/epoch - 5ms/step\n",
      "Epoch 29/200\n",
      "33/33 - 0s - loss: 0.1383 - accuracy: 0.9473 - 167ms/epoch - 5ms/step\n",
      "Epoch 30/200\n",
      "33/33 - 0s - loss: 0.1357 - accuracy: 0.9490 - 170ms/epoch - 5ms/step\n",
      "Epoch 31/200\n",
      "33/33 - 0s - loss: 0.1333 - accuracy: 0.9501 - 170ms/epoch - 5ms/step\n",
      "Epoch 32/200\n",
      "33/33 - 0s - loss: 0.1309 - accuracy: 0.9515 - 177ms/epoch - 5ms/step\n",
      "Epoch 33/200\n",
      "33/33 - 0s - loss: 0.1297 - accuracy: 0.9516 - 180ms/epoch - 5ms/step\n",
      "Epoch 34/200\n",
      "33/33 - 0s - loss: 0.1280 - accuracy: 0.9530 - 168ms/epoch - 5ms/step\n",
      "Epoch 35/200\n",
      "33/33 - 0s - loss: 0.1268 - accuracy: 0.9534 - 198ms/epoch - 6ms/step\n",
      "Epoch 36/200\n",
      "33/33 - 0s - loss: 0.1261 - accuracy: 0.9536 - 177ms/epoch - 5ms/step\n",
      "Epoch 37/200\n",
      "33/33 - 0s - loss: 0.1244 - accuracy: 0.9549 - 168ms/epoch - 5ms/step\n",
      "Epoch 38/200\n",
      "33/33 - 0s - loss: 0.1232 - accuracy: 0.9550 - 163ms/epoch - 5ms/step\n",
      "Epoch 39/200\n",
      "33/33 - 0s - loss: 0.1222 - accuracy: 0.9555 - 161ms/epoch - 5ms/step\n",
      "Epoch 40/200\n",
      "33/33 - 0s - loss: 0.1212 - accuracy: 0.9560 - 161ms/epoch - 5ms/step\n",
      "Epoch 41/200\n",
      "33/33 - 0s - loss: 0.1203 - accuracy: 0.9564 - 164ms/epoch - 5ms/step\n",
      "Epoch 42/200\n",
      "33/33 - 0s - loss: 0.1195 - accuracy: 0.9564 - 171ms/epoch - 5ms/step\n",
      "Epoch 43/200\n",
      "33/33 - 0s - loss: 0.1187 - accuracy: 0.9565 - 169ms/epoch - 5ms/step\n",
      "Epoch 44/200\n",
      "33/33 - 0s - loss: 0.1179 - accuracy: 0.9567 - 159ms/epoch - 5ms/step\n",
      "Epoch 45/200\n",
      "33/33 - 0s - loss: 0.1168 - accuracy: 0.9573 - 161ms/epoch - 5ms/step\n",
      "Epoch 46/200\n",
      "33/33 - 0s - loss: 0.1160 - accuracy: 0.9573 - 160ms/epoch - 5ms/step\n",
      "Epoch 47/200\n",
      "33/33 - 0s - loss: 0.1157 - accuracy: 0.9578 - 165ms/epoch - 5ms/step\n",
      "Epoch 48/200\n",
      "33/33 - 0s - loss: 0.1161 - accuracy: 0.9569 - 161ms/epoch - 5ms/step\n",
      "Epoch 49/200\n",
      "33/33 - 0s - loss: 0.1138 - accuracy: 0.9579 - 168ms/epoch - 5ms/step\n",
      "Epoch 50/200\n",
      "33/33 - 0s - loss: 0.1136 - accuracy: 0.9584 - 195ms/epoch - 6ms/step\n",
      "Epoch 51/200\n",
      "33/33 - 0s - loss: 0.1129 - accuracy: 0.9584 - 163ms/epoch - 5ms/step\n",
      "Epoch 52/200\n",
      "33/33 - 0s - loss: 0.1126 - accuracy: 0.9581 - 169ms/epoch - 5ms/step\n",
      "Epoch 53/200\n",
      "33/33 - 0s - loss: 0.1115 - accuracy: 0.9591 - 166ms/epoch - 5ms/step\n",
      "Epoch 54/200\n",
      "33/33 - 0s - loss: 0.1114 - accuracy: 0.9589 - 168ms/epoch - 5ms/step\n",
      "Epoch 55/200\n",
      "33/33 - 0s - loss: 0.1109 - accuracy: 0.9592 - 164ms/epoch - 5ms/step\n",
      "Epoch 56/200\n",
      "33/33 - 0s - loss: 0.1099 - accuracy: 0.9595 - 167ms/epoch - 5ms/step\n",
      "Epoch 57/200\n",
      "33/33 - 0s - loss: 0.1094 - accuracy: 0.9597 - 164ms/epoch - 5ms/step\n",
      "Epoch 58/200\n",
      "33/33 - 0s - loss: 0.1094 - accuracy: 0.9591 - 162ms/epoch - 5ms/step\n",
      "Epoch 59/200\n",
      "33/33 - 0s - loss: 0.1087 - accuracy: 0.9599 - 166ms/epoch - 5ms/step\n",
      "Epoch 60/200\n",
      "33/33 - 0s - loss: 0.1079 - accuracy: 0.9603 - 169ms/epoch - 5ms/step\n",
      "Epoch 61/200\n",
      "33/33 - 0s - loss: 0.1077 - accuracy: 0.9597 - 162ms/epoch - 5ms/step\n",
      "Epoch 62/200\n",
      "33/33 - 0s - loss: 0.1078 - accuracy: 0.9599 - 164ms/epoch - 5ms/step\n",
      "Epoch 63/200\n",
      "33/33 - 0s - loss: 0.1065 - accuracy: 0.9605 - 162ms/epoch - 5ms/step\n",
      "Epoch 64/200\n",
      "33/33 - 0s - loss: 0.1061 - accuracy: 0.9605 - 161ms/epoch - 5ms/step\n",
      "Epoch 65/200\n",
      "33/33 - 0s - loss: 0.1055 - accuracy: 0.9607 - 167ms/epoch - 5ms/step\n",
      "Epoch 66/200\n",
      "33/33 - 0s - loss: 0.1050 - accuracy: 0.9610 - 163ms/epoch - 5ms/step\n",
      "Epoch 67/200\n",
      "33/33 - 0s - loss: 0.1047 - accuracy: 0.9611 - 168ms/epoch - 5ms/step\n",
      "Epoch 68/200\n",
      "33/33 - 0s - loss: 0.1048 - accuracy: 0.9612 - 168ms/epoch - 5ms/step\n",
      "Epoch 69/200\n",
      "33/33 - 0s - loss: 0.1042 - accuracy: 0.9614 - 162ms/epoch - 5ms/step\n",
      "Epoch 70/200\n",
      "33/33 - 0s - loss: 0.1037 - accuracy: 0.9611 - 159ms/epoch - 5ms/step\n",
      "Epoch 71/200\n",
      "33/33 - 0s - loss: 0.1040 - accuracy: 0.9612 - 166ms/epoch - 5ms/step\n",
      "Epoch 72/200\n",
      "33/33 - 0s - loss: 0.1031 - accuracy: 0.9620 - 164ms/epoch - 5ms/step\n",
      "Epoch 73/200\n",
      "33/33 - 0s - loss: 0.1027 - accuracy: 0.9610 - 162ms/epoch - 5ms/step\n",
      "Epoch 74/200\n",
      "33/33 - 0s - loss: 0.1019 - accuracy: 0.9622 - 177ms/epoch - 5ms/step\n",
      "Epoch 75/200\n",
      "33/33 - 0s - loss: 0.1019 - accuracy: 0.9619 - 169ms/epoch - 5ms/step\n",
      "Epoch 76/200\n",
      "33/33 - 0s - loss: 0.1009 - accuracy: 0.9623 - 174ms/epoch - 5ms/step\n",
      "Epoch 77/200\n",
      "33/33 - 0s - loss: 0.1004 - accuracy: 0.9624 - 176ms/epoch - 5ms/step\n",
      "Epoch 78/200\n",
      "33/33 - 0s - loss: 0.1006 - accuracy: 0.9627 - 161ms/epoch - 5ms/step\n",
      "Epoch 79/200\n",
      "33/33 - 0s - loss: 0.0999 - accuracy: 0.9629 - 167ms/epoch - 5ms/step\n",
      "Epoch 80/200\n",
      "33/33 - 0s - loss: 0.1006 - accuracy: 0.9620 - 162ms/epoch - 5ms/step\n",
      "Epoch 81/200\n",
      "33/33 - 0s - loss: 0.0990 - accuracy: 0.9631 - 165ms/epoch - 5ms/step\n",
      "Epoch 82/200\n",
      "33/33 - 0s - loss: 0.0992 - accuracy: 0.9631 - 161ms/epoch - 5ms/step\n",
      "Epoch 83/200\n",
      "33/33 - 0s - loss: 0.0987 - accuracy: 0.9632 - 160ms/epoch - 5ms/step\n",
      "Epoch 84/200\n",
      "33/33 - 0s - loss: 0.0986 - accuracy: 0.9628 - 163ms/epoch - 5ms/step\n",
      "Epoch 85/200\n",
      "33/33 - 0s - loss: 0.0986 - accuracy: 0.9630 - 170ms/epoch - 5ms/step\n",
      "Epoch 86/200\n",
      "33/33 - 0s - loss: 0.0979 - accuracy: 0.9633 - 163ms/epoch - 5ms/step\n",
      "Epoch 87/200\n",
      "33/33 - 0s - loss: 0.0974 - accuracy: 0.9634 - 161ms/epoch - 5ms/step\n",
      "Epoch 88/200\n",
      "33/33 - 0s - loss: 0.0976 - accuracy: 0.9632 - 164ms/epoch - 5ms/step\n",
      "Epoch 89/200\n",
      "33/33 - 0s - loss: 0.0970 - accuracy: 0.9639 - 162ms/epoch - 5ms/step\n",
      "Epoch 90/200\n",
      "33/33 - 0s - loss: 0.0969 - accuracy: 0.9640 - 161ms/epoch - 5ms/step\n",
      "Epoch 91/200\n",
      "33/33 - 0s - loss: 0.0978 - accuracy: 0.9628 - 171ms/epoch - 5ms/step\n",
      "Epoch 92/200\n",
      "33/33 - 0s - loss: 0.0964 - accuracy: 0.9638 - 196ms/epoch - 6ms/step\n",
      "Epoch 93/200\n",
      "33/33 - 0s - loss: 0.0959 - accuracy: 0.9638 - 164ms/epoch - 5ms/step\n",
      "Epoch 94/200\n",
      "33/33 - 0s - loss: 0.0961 - accuracy: 0.9639 - 186ms/epoch - 6ms/step\n",
      "Epoch 95/200\n",
      "33/33 - 0s - loss: 0.0963 - accuracy: 0.9638 - 168ms/epoch - 5ms/step\n",
      "Epoch 96/200\n",
      "33/33 - 0s - loss: 0.0955 - accuracy: 0.9645 - 165ms/epoch - 5ms/step\n",
      "Epoch 97/200\n",
      "33/33 - 0s - loss: 0.0952 - accuracy: 0.9643 - 162ms/epoch - 5ms/step\n",
      "Epoch 98/200\n",
      "33/33 - 0s - loss: 0.0951 - accuracy: 0.9640 - 162ms/epoch - 5ms/step\n",
      "Epoch 99/200\n",
      "33/33 - 0s - loss: 0.0952 - accuracy: 0.9643 - 162ms/epoch - 5ms/step\n",
      "Epoch 100/200\n",
      "33/33 - 0s - loss: 0.0960 - accuracy: 0.9634 - 167ms/epoch - 5ms/step\n",
      "Epoch 101/200\n",
      "33/33 - 0s - loss: 0.0948 - accuracy: 0.9642 - 165ms/epoch - 5ms/step\n",
      "Epoch 102/200\n",
      "33/33 - 0s - loss: 0.0943 - accuracy: 0.9644 - 164ms/epoch - 5ms/step\n",
      "Epoch 103/200\n",
      "33/33 - 0s - loss: 0.0940 - accuracy: 0.9645 - 167ms/epoch - 5ms/step\n",
      "Epoch 104/200\n",
      "33/33 - 0s - loss: 0.0940 - accuracy: 0.9645 - 162ms/epoch - 5ms/step\n",
      "Epoch 105/200\n",
      "33/33 - 0s - loss: 0.0938 - accuracy: 0.9647 - 161ms/epoch - 5ms/step\n",
      "Epoch 106/200\n",
      "33/33 - 0s - loss: 0.0935 - accuracy: 0.9645 - 166ms/epoch - 5ms/step\n",
      "Epoch 107/200\n",
      "33/33 - 0s - loss: 0.0934 - accuracy: 0.9644 - 165ms/epoch - 5ms/step\n",
      "Epoch 108/200\n",
      "33/33 - 0s - loss: 0.0941 - accuracy: 0.9640 - 163ms/epoch - 5ms/step\n",
      "Epoch 109/200\n",
      "33/33 - 0s - loss: 0.0932 - accuracy: 0.9648 - 165ms/epoch - 5ms/step\n",
      "Epoch 110/200\n",
      "33/33 - 0s - loss: 0.0928 - accuracy: 0.9645 - 163ms/epoch - 5ms/step\n",
      "Epoch 111/200\n",
      "33/33 - 0s - loss: 0.0926 - accuracy: 0.9648 - 168ms/epoch - 5ms/step\n",
      "Epoch 112/200\n",
      "33/33 - 0s - loss: 0.0925 - accuracy: 0.9650 - 166ms/epoch - 5ms/step\n",
      "Epoch 113/200\n",
      "33/33 - 0s - loss: 0.0923 - accuracy: 0.9650 - 167ms/epoch - 5ms/step\n",
      "Epoch 114/200\n",
      "33/33 - 0s - loss: 0.0926 - accuracy: 0.9650 - 166ms/epoch - 5ms/step\n",
      "Epoch 115/200\n",
      "33/33 - 0s - loss: 0.0920 - accuracy: 0.9654 - 166ms/epoch - 5ms/step\n",
      "Epoch 116/200\n",
      "33/33 - 0s - loss: 0.0922 - accuracy: 0.9649 - 161ms/epoch - 5ms/step\n",
      "Epoch 117/200\n",
      "33/33 - 0s - loss: 0.0927 - accuracy: 0.9644 - 173ms/epoch - 5ms/step\n",
      "Epoch 118/200\n",
      "33/33 - 0s - loss: 0.0922 - accuracy: 0.9649 - 175ms/epoch - 5ms/step\n",
      "Epoch 119/200\n",
      "33/33 - 0s - loss: 0.0914 - accuracy: 0.9646 - 170ms/epoch - 5ms/step\n",
      "Epoch 120/200\n",
      "33/33 - 0s - loss: 0.0916 - accuracy: 0.9644 - 167ms/epoch - 5ms/step\n",
      "Epoch 121/200\n",
      "33/33 - 0s - loss: 0.0914 - accuracy: 0.9653 - 165ms/epoch - 5ms/step\n",
      "Epoch 122/200\n",
      "33/33 - 0s - loss: 0.0907 - accuracy: 0.9653 - 161ms/epoch - 5ms/step\n",
      "Epoch 123/200\n",
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      "Epoch 124/200\n",
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      "Epoch 125/200\n",
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      "Epoch 126/200\n",
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      "Epoch 127/200\n",
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      "Epoch 128/200\n",
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      "Epoch 129/200\n",
      "33/33 - 0s - loss: 0.0901 - accuracy: 0.9650 - 165ms/epoch - 5ms/step\n",
      "Epoch 130/200\n",
      "33/33 - 0s - loss: 0.0898 - accuracy: 0.9656 - 164ms/epoch - 5ms/step\n",
      "Epoch 131/200\n",
      "33/33 - 0s - loss: 0.0905 - accuracy: 0.9650 - 163ms/epoch - 5ms/step\n",
      "Epoch 132/200\n",
      "33/33 - 0s - loss: 0.0900 - accuracy: 0.9650 - 167ms/epoch - 5ms/step\n",
      "Epoch 133/200\n",
      "33/33 - 0s - loss: 0.0896 - accuracy: 0.9653 - 161ms/epoch - 5ms/step\n",
      "Epoch 134/200\n",
      "33/33 - 0s - loss: 0.0893 - accuracy: 0.9655 - 160ms/epoch - 5ms/step\n",
      "Epoch 135/200\n",
      "33/33 - 0s - loss: 0.0912 - accuracy: 0.9646 - 160ms/epoch - 5ms/step\n",
      "Epoch 136/200\n",
      "33/33 - 0s - loss: 0.0893 - accuracy: 0.9657 - 166ms/epoch - 5ms/step\n",
      "Epoch 137/200\n",
      "33/33 - 0s - loss: 0.0891 - accuracy: 0.9656 - 164ms/epoch - 5ms/step\n",
      "Epoch 138/200\n",
      "33/33 - 0s - loss: 0.0888 - accuracy: 0.9654 - 163ms/epoch - 5ms/step\n",
      "Epoch 139/200\n",
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      "Epoch 145/200\n",
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      "Epoch 146/200\n",
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      "Epoch 150/200\n",
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      "Epoch 155/200\n",
      "33/33 - 0s - loss: 0.0883 - accuracy: 0.9659 - 164ms/epoch - 5ms/step\n",
      "Epoch 156/200\n",
      "33/33 - 0s - loss: 0.0876 - accuracy: 0.9654 - 180ms/epoch - 5ms/step\n",
      "Epoch 157/200\n",
      "33/33 - 0s - loss: 0.0883 - accuracy: 0.9655 - 164ms/epoch - 5ms/step\n",
      "Epoch 158/200\n",
      "33/33 - 0s - loss: 0.0873 - accuracy: 0.9661 - 169ms/epoch - 5ms/step\n",
      "Epoch 159/200\n",
      "33/33 - 0s - loss: 0.0868 - accuracy: 0.9665 - 162ms/epoch - 5ms/step\n",
      "Epoch 160/200\n",
      "33/33 - 0s - loss: 0.0875 - accuracy: 0.9660 - 164ms/epoch - 5ms/step\n",
      "Epoch 161/200\n",
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      "Epoch 162/200\n",
      "33/33 - 0s - loss: 0.0871 - accuracy: 0.9662 - 172ms/epoch - 5ms/step\n",
      "Epoch 163/200\n",
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      "Epoch 165/200\n",
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      "Epoch 166/200\n",
      "33/33 - 0s - loss: 0.0867 - accuracy: 0.9663 - 173ms/epoch - 5ms/step\n",
      "Epoch 167/200\n",
      "33/33 - 0s - loss: 0.0871 - accuracy: 0.9662 - 173ms/epoch - 5ms/step\n",
      "Epoch 168/200\n",
      "33/33 - 0s - loss: 0.0866 - accuracy: 0.9658 - 165ms/epoch - 5ms/step\n",
      "Epoch 169/200\n",
      "33/33 - 0s - loss: 0.0865 - accuracy: 0.9662 - 164ms/epoch - 5ms/step\n",
      "Epoch 170/200\n",
      "33/33 - 0s - loss: 0.0862 - accuracy: 0.9665 - 162ms/epoch - 5ms/step\n",
      "Epoch 171/200\n",
      "33/33 - 0s - loss: 0.0866 - accuracy: 0.9661 - 174ms/epoch - 5ms/step\n",
      "Epoch 172/200\n",
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      "Epoch 173/200\n",
      "33/33 - 0s - loss: 0.0863 - accuracy: 0.9665 - 189ms/epoch - 6ms/step\n",
      "Epoch 174/200\n",
      "33/33 - 0s - loss: 0.0864 - accuracy: 0.9664 - 186ms/epoch - 6ms/step\n",
      "Epoch 175/200\n",
      "33/33 - 0s - loss: 0.0855 - accuracy: 0.9669 - 183ms/epoch - 6ms/step\n",
      "Epoch 176/200\n",
      "33/33 - 0s - loss: 0.0855 - accuracy: 0.9668 - 181ms/epoch - 5ms/step\n",
      "Epoch 177/200\n",
      "33/33 - 0s - loss: 0.0860 - accuracy: 0.9669 - 171ms/epoch - 5ms/step\n",
      "Epoch 178/200\n",
      "33/33 - 0s - loss: 0.0866 - accuracy: 0.9661 - 163ms/epoch - 5ms/step\n",
      "Epoch 179/200\n",
      "33/33 - 0s - loss: 0.0870 - accuracy: 0.9657 - 163ms/epoch - 5ms/step\n",
      "Epoch 180/200\n",
      "33/33 - 0s - loss: 0.0858 - accuracy: 0.9665 - 169ms/epoch - 5ms/step\n",
      "Epoch 181/200\n",
      "33/33 - 0s - loss: 0.0860 - accuracy: 0.9665 - 180ms/epoch - 5ms/step\n",
      "Epoch 182/200\n",
      "33/33 - 0s - loss: 0.0853 - accuracy: 0.9672 - 176ms/epoch - 5ms/step\n",
      "Epoch 183/200\n",
      "33/33 - 0s - loss: 0.0862 - accuracy: 0.9660 - 184ms/epoch - 6ms/step\n",
      "Epoch 184/200\n",
      "33/33 - 0s - loss: 0.0856 - accuracy: 0.9669 - 177ms/epoch - 5ms/step\n",
      "Epoch 185/200\n",
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      "Epoch 186/200\n",
      "33/33 - 0s - loss: 0.0860 - accuracy: 0.9664 - 191ms/epoch - 6ms/step\n",
      "Epoch 187/200\n",
      "33/33 - 0s - loss: 0.0857 - accuracy: 0.9658 - 192ms/epoch - 6ms/step\n",
      "Epoch 188/200\n",
      "33/33 - 0s - loss: 0.0850 - accuracy: 0.9671 - 164ms/epoch - 5ms/step\n",
      "Epoch 189/200\n",
      "33/33 - 0s - loss: 0.0850 - accuracy: 0.9669 - 165ms/epoch - 5ms/step\n",
      "Epoch 190/200\n",
      "33/33 - 0s - loss: 0.0853 - accuracy: 0.9668 - 170ms/epoch - 5ms/step\n",
      "Epoch 191/200\n",
      "33/33 - 0s - loss: 0.0850 - accuracy: 0.9669 - 171ms/epoch - 5ms/step\n",
      "Epoch 192/200\n",
      "33/33 - 0s - loss: 0.0848 - accuracy: 0.9668 - 177ms/epoch - 5ms/step\n",
      "Epoch 193/200\n",
      "33/33 - 0s - loss: 0.0845 - accuracy: 0.9669 - 168ms/epoch - 5ms/step\n",
      "Epoch 194/200\n",
      "33/33 - 0s - loss: 0.0847 - accuracy: 0.9664 - 165ms/epoch - 5ms/step\n",
      "Epoch 195/200\n",
      "33/33 - 0s - loss: 0.0844 - accuracy: 0.9671 - 164ms/epoch - 5ms/step\n",
      "Epoch 196/200\n",
      "33/33 - 0s - loss: 0.0847 - accuracy: 0.9667 - 163ms/epoch - 5ms/step\n",
      "Epoch 197/200\n",
      "33/33 - 0s - loss: 0.0861 - accuracy: 0.9653 - 164ms/epoch - 5ms/step\n",
      "Epoch 198/200\n",
      "33/33 - 0s - loss: 0.0855 - accuracy: 0.9666 - 161ms/epoch - 5ms/step\n",
      "Epoch 199/200\n",
      "33/33 - 0s - loss: 0.0854 - accuracy: 0.9665 - 161ms/epoch - 5ms/step\n",
      "Epoch 200/200\n",
      "33/33 - 0s - loss: 0.0857 - accuracy: 0.9662 - 164ms/epoch - 5ms/step\n"
     ]
    }
   ],
   "source": [
    "def build_model():\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(20, return_sequences=True,input_shape=(1,56)))\n",
    "    model.add(LSTM(20, return_sequences=True))\n",
    "    model.add(Dense(10, activation='softmax')) #for multiclass classification\n",
    "    #Compile the model\n",
    "    model.compile(loss='sparse_categorical_crossentropy', optimizer='adam',\n",
    "                  # metrics=['accuracy',f1_m,precision_m, recall_m]\n",
    "                  metrics=['accuracy']\n",
    "                 )\n",
    "    return model\n",
    "\n",
    "#The LSTM input layer must be 3D.\n",
    "#The meaning of the 3 input dimensions are: samples, time steps, and features.\n",
    "#reshape input data\n",
    "X_train_array = np.array(X_train) #array has been declared in the previous cell\n",
    "print(len(X_train_array))\n",
    "X_train_reshaped = X_train_array.reshape(X_train_array.shape[0],1,56)\n",
    "\n",
    "#reshape output data\n",
    "X_test_array=  np.array(X_test)\n",
    "X_test_reshaped = X_test_array.reshape(X_test_array.shape[0],1,56) \n",
    "\n",
    "\n",
    "#institate the model\n",
    "model = build_model()\n",
    "\n",
    "\n",
    "#fit the model\n",
    "start = time.time()\n",
    "model.fit(X_train_reshaped, y_train, epochs=200, batch_size=2000,verbose=2)\n",
    "end_train = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "id": "1da88898",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-10T04:29:50.850959Z",
     "iopub.status.busy": "2022-11-10T04:29:50.850580Z",
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     "shell.execute_reply": "2022-11-10T04:29:52.726119Z"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "515/515 [==============================] - 1s 1ms/step - loss: 0.0896 - accuracy: 0.9644\n"
     ]
    }
   ],
   "source": [
    "#Evaluate the neural network\n",
    "loss, accuracy = model.evaluate(X_test_reshaped, y_test)\n",
    "# loss, accuracy, f1s, precision, recall = model.evaluate(X_test_reshaped, y_test)\n",
    "end_predict = time.time()\n",
    "model_performance.loc['LSTM (Keras)'] = [accuracy, accuracy, accuracy, accuracy,end_train-start,end_predict-end_train,end_predict-start]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6835d627",
   "metadata": {
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     "end_time": "2022-11-10T04:29:53.824336",
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     "status": "completed"
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    "tags": []
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   "source": [
    "<a id='5'></a>\n",
    "# <p style=\"padding: 8px;color:white; display:fill;background-color:#555555; border-radius:5px; font-size:100%\"> <b>Evaluate</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8daead70",
   "metadata": {
    "papermill": {
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     "end_time": "2022-11-10T04:29:54.888698",
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     "start_time": "2022-11-10T04:29:54.355183",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "The models are compared in this chapter to determine which give the best performance. It seems that the winner is the Random Forest with a good performance on speed and prediction. \n",
    "\n",
    "The MLP takes much longer to train in Keras than through sci-kit learn. I don't think that the verbosity of the output could have such a big impact. It is unclear why Keras is underperforming. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "id": "e206d191",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2022-11-10T04:29:56.042529Z",
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     "shell.execute_reply": "2022-11-10T04:29:56.069615Z"
    },
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     "exception": false,
     "start_time": "2022-11-10T04:29:55.423004",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_4e0ff_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_4e0ff_level0_col1\" class=\"col_heading level0 col1\" >Recall</th>\n",
       "      <th id=\"T_4e0ff_level0_col2\" class=\"col_heading level0 col2\" >Precision</th>\n",
       "      <th id=\"T_4e0ff_level0_col3\" class=\"col_heading level0 col3\" >F1-Score</th>\n",
       "      <th id=\"T_4e0ff_level0_col4\" class=\"col_heading level0 col4\" >time to train</th>\n",
       "      <th id=\"T_4e0ff_level0_col5\" class=\"col_heading level0 col5\" >time to predict</th>\n",
       "      <th id=\"T_4e0ff_level0_col6\" class=\"col_heading level0 col6\" >total time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4e0ff_level0_row0\" class=\"row_heading level0 row0\" >Logistic</th>\n",
       "      <td id=\"T_4e0ff_row0_col0\" class=\"data row0 col0\" >92.83%</td>\n",
       "      <td id=\"T_4e0ff_row0_col1\" class=\"data row0 col1\" >92.83%</td>\n",
       "      <td id=\"T_4e0ff_row0_col2\" class=\"data row0 col2\" >92.87%</td>\n",
       "      <td id=\"T_4e0ff_row0_col3\" class=\"data row0 col3\" >92.84%</td>\n",
       "      <td id=\"T_4e0ff_row0_col4\" class=\"data row0 col4\" >0.6</td>\n",
       "      <td id=\"T_4e0ff_row0_col5\" class=\"data row0 col5\" >0.0</td>\n",
       "      <td id=\"T_4e0ff_row0_col6\" class=\"data row0 col6\" >0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e0ff_level0_row1\" class=\"row_heading level0 row1\" >kNN</th>\n",
       "      <td id=\"T_4e0ff_row1_col0\" class=\"data row1 col0\" >95.04%</td>\n",
       "      <td id=\"T_4e0ff_row1_col1\" class=\"data row1 col1\" >95.04%</td>\n",
       "      <td id=\"T_4e0ff_row1_col2\" class=\"data row1 col2\" >95.09%</td>\n",
       "      <td id=\"T_4e0ff_row1_col3\" class=\"data row1 col3\" >95.05%</td>\n",
       "      <td id=\"T_4e0ff_row1_col4\" class=\"data row1 col4\" >0.0</td>\n",
       "      <td id=\"T_4e0ff_row1_col5\" class=\"data row1 col5\" >1.1</td>\n",
       "      <td id=\"T_4e0ff_row1_col6\" class=\"data row1 col6\" >1.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e0ff_level0_row2\" class=\"row_heading level0 row2\" >Decision Tree</th>\n",
       "      <td id=\"T_4e0ff_row2_col0\" class=\"data row2 col0\" >96.45%</td>\n",
       "      <td id=\"T_4e0ff_row2_col1\" class=\"data row2 col1\" >96.45%</td>\n",
       "      <td id=\"T_4e0ff_row2_col2\" class=\"data row2 col2\" >96.45%</td>\n",
       "      <td id=\"T_4e0ff_row2_col3\" class=\"data row2 col3\" >96.45%</td>\n",
       "      <td id=\"T_4e0ff_row2_col4\" class=\"data row2 col4\" >0.9</td>\n",
       "      <td id=\"T_4e0ff_row2_col5\" class=\"data row2 col5\" >0.0</td>\n",
       "      <td id=\"T_4e0ff_row2_col6\" class=\"data row2 col6\" >0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e0ff_level0_row3\" class=\"row_heading level0 row3\" >Extra Trees</th>\n",
       "      <td id=\"T_4e0ff_row3_col0\" class=\"data row3 col0\" >97.53%</td>\n",
       "      <td id=\"T_4e0ff_row3_col1\" class=\"data row3 col1\" >97.53%</td>\n",
       "      <td id=\"T_4e0ff_row3_col2\" class=\"data row3 col2\" >97.55%</td>\n",
       "      <td id=\"T_4e0ff_row3_col3\" class=\"data row3 col3\" >97.53%</td>\n",
       "      <td id=\"T_4e0ff_row3_col4\" class=\"data row3 col4\" >1.3</td>\n",
       "      <td id=\"T_4e0ff_row3_col5\" class=\"data row3 col5\" >0.1</td>\n",
       "      <td id=\"T_4e0ff_row3_col6\" class=\"data row3 col6\" >1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e0ff_level0_row4\" class=\"row_heading level0 row4\" >Random Forest</th>\n",
       "      <td id=\"T_4e0ff_row4_col0\" class=\"data row4 col0\" >97.68%</td>\n",
       "      <td id=\"T_4e0ff_row4_col1\" class=\"data row4 col1\" >97.68%</td>\n",
       "      <td id=\"T_4e0ff_row4_col2\" class=\"data row4 col2\" >97.69%</td>\n",
       "      <td id=\"T_4e0ff_row4_col3\" class=\"data row4 col3\" >97.68%</td>\n",
       "      <td id=\"T_4e0ff_row4_col4\" class=\"data row4 col4\" >1.5</td>\n",
       "      <td id=\"T_4e0ff_row4_col5\" class=\"data row4 col5\" >0.0</td>\n",
       "      <td id=\"T_4e0ff_row4_col6\" class=\"data row4 col6\" >1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e0ff_level0_row5\" class=\"row_heading level0 row5\" >Gradient Boosting Classifier</th>\n",
       "      <td id=\"T_4e0ff_row5_col0\" class=\"data row5 col0\" >95.85%</td>\n",
       "      <td id=\"T_4e0ff_row5_col1\" class=\"data row5 col1\" >95.85%</td>\n",
       "      <td id=\"T_4e0ff_row5_col2\" class=\"data row5 col2\" >95.86%</td>\n",
       "      <td id=\"T_4e0ff_row5_col3\" class=\"data row5 col3\" >95.85%</td>\n",
       "      <td id=\"T_4e0ff_row5_col4\" class=\"data row5 col4\" >28.4</td>\n",
       "      <td id=\"T_4e0ff_row5_col5\" class=\"data row5 col5\" >0.0</td>\n",
       "      <td id=\"T_4e0ff_row5_col6\" class=\"data row5 col6\" >28.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e0ff_level0_row6\" class=\"row_heading level0 row6\" >MLP</th>\n",
       "      <td id=\"T_4e0ff_row6_col0\" class=\"data row6 col0\" >96.17%</td>\n",
       "      <td id=\"T_4e0ff_row6_col1\" class=\"data row6 col1\" >96.17%</td>\n",
       "      <td id=\"T_4e0ff_row6_col2\" class=\"data row6 col2\" >96.23%</td>\n",
       "      <td id=\"T_4e0ff_row6_col3\" class=\"data row6 col3\" >96.17%</td>\n",
       "      <td id=\"T_4e0ff_row6_col4\" class=\"data row6 col4\" >12.0</td>\n",
       "      <td id=\"T_4e0ff_row6_col5\" class=\"data row6 col5\" >0.0</td>\n",
       "      <td id=\"T_4e0ff_row6_col6\" class=\"data row6 col6\" >12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e0ff_level0_row7\" class=\"row_heading level0 row7\" >MLP (Keras)</th>\n",
       "      <td id=\"T_4e0ff_row7_col0\" class=\"data row7 col0\" >96.22%</td>\n",
       "      <td id=\"T_4e0ff_row7_col1\" class=\"data row7 col1\" >96.22%</td>\n",
       "      <td id=\"T_4e0ff_row7_col2\" class=\"data row7 col2\" >96.22%</td>\n",
       "      <td id=\"T_4e0ff_row7_col3\" class=\"data row7 col3\" >96.22%</td>\n",
       "      <td id=\"T_4e0ff_row7_col4\" class=\"data row7 col4\" >16.0</td>\n",
       "      <td id=\"T_4e0ff_row7_col5\" class=\"data row7 col5\" >0.7</td>\n",
       "      <td id=\"T_4e0ff_row7_col6\" class=\"data row7 col6\" >16.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e0ff_level0_row8\" class=\"row_heading level0 row8\" >GRU (Keras)</th>\n",
       "      <td id=\"T_4e0ff_row8_col0\" class=\"data row8 col0\" >96.58%</td>\n",
       "      <td id=\"T_4e0ff_row8_col1\" class=\"data row8 col1\" >96.58%</td>\n",
       "      <td id=\"T_4e0ff_row8_col2\" class=\"data row8 col2\" >96.58%</td>\n",
       "      <td id=\"T_4e0ff_row8_col3\" class=\"data row8 col3\" >96.58%</td>\n",
       "      <td id=\"T_4e0ff_row8_col4\" class=\"data row8 col4\" >38.2</td>\n",
       "      <td id=\"T_4e0ff_row8_col5\" class=\"data row8 col5\" >1.4</td>\n",
       "      <td id=\"T_4e0ff_row8_col6\" class=\"data row8 col6\" >39.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e0ff_level0_row9\" class=\"row_heading level0 row9\" >LSTM (Keras)</th>\n",
       "      <td id=\"T_4e0ff_row9_col0\" class=\"data row9 col0\" >96.44%</td>\n",
       "      <td id=\"T_4e0ff_row9_col1\" class=\"data row9 col1\" >96.44%</td>\n",
       "      <td id=\"T_4e0ff_row9_col2\" class=\"data row9 col2\" >96.44%</td>\n",
       "      <td id=\"T_4e0ff_row9_col3\" class=\"data row9 col3\" >96.44%</td>\n",
       "      <td id=\"T_4e0ff_row9_col4\" class=\"data row9 col4\" >36.9</td>\n",
       "      <td id=\"T_4e0ff_row9_col5\" class=\"data row9 col5\" >1.4</td>\n",
       "      <td id=\"T_4e0ff_row9_col6\" class=\"data row9 col6\" >38.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1aa9223a650>"
      ]
     },
     "execution_count": 521,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_performance.fillna(.90,inplace=True)\n",
    "model_performance.style.background_gradient(cmap='coolwarm').format({'Accuracy': '{:.2%}',\n",
    "                                                                     'Precision': '{:.2%}',\n",
    "                                                                     'Recall': '{:.2%}',\n",
    "                                                                     'F1-Score': '{:.2%}',\n",
    "                                                                     'time to train':'{:.1f}',\n",
    "                                                                     'time to predict':'{:.1f}',\n",
    "                                                                     'total time':'{:.1f}',\n",
    "                                                                     })"
   ]
  }
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